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WO2024177073A1 - Élément optique, cytomètre en flux, dispositif d'optimisation de motif de point lumineux, procédé d'optimisation de motif de point lumineux et programme - Google Patents

Élément optique, cytomètre en flux, dispositif d'optimisation de motif de point lumineux, procédé d'optimisation de motif de point lumineux et programme Download PDF

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Publication number
WO2024177073A1
WO2024177073A1 PCT/JP2024/006080 JP2024006080W WO2024177073A1 WO 2024177073 A1 WO2024177073 A1 WO 2024177073A1 JP 2024006080 W JP2024006080 W JP 2024006080W WO 2024177073 A1 WO2024177073 A1 WO 2024177073A1
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WIPO (PCT)
Prior art keywords
bright spot
pattern
spot pattern
measurement object
light
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PCT/JP2024/006080
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English (en)
Japanese (ja)
Inventor
博文 中山
一誠 佐藤
啓晃 安達
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シンクサイト株式会社
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Publication of WO2024177073A1 publication Critical patent/WO2024177073A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1434—Optical arrangements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated

Definitions

  • the present invention relates to an optical element, a flow cytometer, a bright spot pattern optimization device, a bright spot pattern optimization method, and a program.
  • the Ghost Motion Imaging (GMI) method is known as a method for extracting morphological information of a measurement object as optical information.
  • a spatial light modulation element such as a diffractive optical element (DOE) is placed in the optical path between a light source and the measurement object, and the illumination light from the light source is processed into illumination light having a specific illumination pattern, which is then irradiated onto the measurement object moving through the flow path.
  • DOE diffractive optical element
  • the illumination pattern is composed of an arrangement of multiple areas with different optical properties.
  • the illumination pattern used in the GMI method can be provided as a binary pattern consisting of light-transmitting areas and light-blocking areas.
  • binary patterns in which light-transmitting areas (bright spots) are irregularly (randomly) arranged have often been used as illumination patterns in structured illumination light.
  • light-transmitting areas (bright spots) are irregularly (randomly) arranged
  • Patent Document 1 a mask structure optimization method is known that optimizes the illumination pattern used in the GMI method. Also, a method is known that optimizes the modulation pattern using a genetic algorithm (Patent Document 2).
  • Patent Document 1 and Patent Document 2 are methods for optimizing the illumination pattern for a measurement object having a specific size and shape.
  • a highly versatile bright spot pattern that achieves high classification accuracy regardless of the size and shape of the measurement object is also required for flow cytometers using GMI.
  • flow cytometers using the GMI method there is a demand for improving classification accuracy for a wide range of measurement objects by using a bright spot pattern imparted to the illumination light or modulated light from the measurement object.
  • the present invention has been made in consideration of the above points, and provides an optical element, a flow cytometer, a bright spot pattern optimization device, a bright spot pattern optimization method, and a program that can improve the classification accuracy of a flow cytometer.
  • the present invention has been made to solve the above problems, and one aspect of the present invention is a measurement method for irradiating a moving measurement object with illumination light and detecting modulated light from the measurement object irradiated with the illumination light, the measurement method detecting morphological information of the measurement object as optical information by imparting a bright spot pattern to either the illumination light or the modulated light, the optical element having a plurality of regions with mutually different optical properties and imparting the bright spot pattern according to the distribution of the plurality of regions, the bright spot pattern including at least two partial patterns arranged at mutually different positions in the direction in which the measurement object moves, and at least one of the partial patterns being a pattern based on a Fourier basis.
  • At least two of the partial patterns are patterns based on a Fourier basis and are different from each other.
  • At least two of the partial patterns based on a Fourier basis are arranged adjacent to each other in the direction in which the measurement object moves.
  • the partial patterns are patterns based on a Fourier basis and are different from each other.
  • An aspect of the present invention is a flow cytometer comprising the above optical element, a light source that emits the illumination light toward the object to be measured, a microfluidic device having a flow path through which the object to be measured can flow together with a fluid, a photodetector that detects, in a time series manner, an optical signal intensity that is the intensity of the modulated light emitted from the object to be measured, and an information generating device that generates optical information that indicates the shape of the object to be measured based on the optical signal intensity detected by the photodetector, wherein the optical element is placed midway along the optical path from the light source to the object to be measured, and the object to be measured is irradiated with the illumination light to which the bright spot pattern has been imparted by the optical element.
  • Another aspect of the present invention is a flow cytometer comprising the above optical element, a light source that emits the illumination light toward the object to be measured, a microfluidic device having a flow path through which the object to be measured can flow together with a fluid, a photodetector that detects, in a time series manner, an optical signal intensity that is the intensity of the modulated light emitted from the object to be measured, and an information generating device that generates optical information that indicates the shape of the object to be measured based on the optical signal intensity detected by the photodetector, wherein the optical element is installed midway along the optical path from the object to be measured to the photodetector, and the modulated light modulated by the object to be measured is given the bright spot pattern by the optical element.
  • Another aspect of the present invention is a measurement method for irradiating a moving measurement object with illumination light and detecting modulated light from the measurement object irradiated with the illumination light, and a device for optimizing a bright spot pattern in the measurement method for detecting morphological information of the measurement object as optical information by applying a bright spot pattern to either the illumination light or the modulated light using an optical element, the bright spot pattern optimization device determining a bright spot pattern with the highest classification accuracy for the measurement object from among multiple bright spot patterns based on multiple Fourier bases, and adding the bright spot pattern determined to have the highest classification accuracy to an already added bright spot pattern so as to be positioned adjacent to the direction in which the measurement object moves.
  • the classification accuracy is evaluated for bright spot patterns that are combinations of each bright spot pattern and an already added bright spot pattern, and the bright spot pattern with the highest classification accuracy is determined from among the multiple bright spot patterns.
  • Another aspect of the present invention is a measurement method for irradiating a moving measurement object with illumination light and detecting modulated light from the measurement object irradiated with the illumination light, in which morphological information of the measurement object is detected as optical information by applying a bright spot pattern to either the illumination light or the modulated light by an optical element, the method being a method for generating a bright spot pattern, the bright spot pattern optimization method having a determination step of determining a bright spot pattern with the highest classification accuracy for the morphological information of the measurement object from among a plurality of bright spot patterns based on a plurality of Fourier bases, and an addition step of adding the bright spot pattern determined in the determination step to have the highest classification accuracy to an already added bright spot pattern so as to be positioned adjacent to the direction in which the measurement object moves.
  • Another aspect of the present invention is a measurement method for irradiating a moving measurement object with illumination light and detecting modulated light from the measurement object irradiated with the illumination light, in which morphological information of the measurement object is detected as optical information by applying a bright spot pattern to either the illumination light or the modulated light by an optical element
  • the measurement method being a program for causing a computer that generates bright spot patterns to execute a determination step of determining a bright spot pattern that has the highest classification accuracy for the morphological information of the measurement object from among multiple bright spot patterns based on multiple Fourier bases, and an addition step of adding the bright spot pattern determined in the determination step to have the highest classification accuracy to an already added bright spot pattern so as to be positioned adjacent to the measurement object in the direction in which the measurement object moves.
  • the present invention can improve the classification accuracy of a flow cytometer.
  • FIG. 1 is a diagram showing an example of the configuration of a flow cytometer according to a first embodiment of the present invention.
  • FIG. 2 is a diagram illustrating an example of a configuration of a spatial light modulation unit according to the first embodiment of the present invention.
  • FIG. 2 is a diagram showing an example of a structured illumination pattern according to the first embodiment of the present invention.
  • 4 is a diagram showing an example of a Fourier basis used to generate partial patterns that constitute a structured illumination pattern according to the first embodiment of the present invention.
  • FIG. 1 is a diagram illustrating an example of a configuration of an information generating device according to a first embodiment of the present invention.
  • 5A to 5C are diagrams illustrating an example of a bright spot pattern generation process according to the first embodiment of the present invention.
  • FIG. 2 is a diagram illustrating an example of a Fourier basis according to the first embodiment of the present invention.
  • FIG. 2 is a diagram showing an example of a bright spot arrangement region according to the first embodiment of the present invention.
  • FIG. 2 is a diagram showing an example of a bright spot pattern according to the first embodiment of the present invention.
  • FIG. 2 is a diagram illustrating an example of a combination optimization process according to the first embodiment of the present invention.
  • FIG. 2 is a diagram illustrating an example of a Fourier basis according to the first embodiment of the present invention.
  • FIG. 2 is a diagram illustrating an example of a Fourier basis according to the first embodiment of the present invention.
  • FIG. 4 is a diagram showing an example of a generated bright spot pattern according to the first embodiment of the present invention.
  • FIG. 4 is a diagram showing an example of a generated bright spot pattern according to the first embodiment of the present invention.
  • FIG. 11 is a diagram showing an example of a bright spot pattern of a frequency determined to have the highest classification accuracy according to the first embodiment of the present invention.
  • FIG. 2 is a diagram showing an example of a plurality of combined bright spot patterns according to the first embodiment of the present invention.
  • FIG. 2 is a diagram showing an example of a plurality of combined bright spot patterns according to the first embodiment of the present invention.
  • FIG. 2 is a diagram showing an example of a cell image used for evaluating classification accuracy according to the first embodiment of the present invention.
  • FIG. 4 is a diagram showing an example of a combination of Fourier bases used in a generated structured illumination pattern according to the first embodiment of the present invention.
  • FIG. 2 is a diagram showing an example of a bright spot pattern generated by sampling from a selected Fourier basis according to the first embodiment of the present invention.
  • FIG. 11 is a diagram showing an example of a result of evaluating classification accuracy of a structured illumination pattern obtained as a result of optimization according to the first embodiment of the present invention.
  • FIG. 11 is a diagram showing an example of the configuration of a flow cytometer according to a second embodiment of the present invention.
  • FIG. 1 is a diagram showing an example of the configuration of a flow cytometer 1 according to this embodiment.
  • the flow cytometer 1 includes a microfluidic device 2, a light source 3, a spatial light modulation unit 4, a light detection optical system 5, a photodetector 6, a DAQ (Data Acquisition) device 7, and a personal computer (PC: Personal Computer) 8.
  • PC Personal Computer
  • the microfluidic device 2 includes a flow channel 20 through which cells C can flow together with the fluid.
  • the flow rate of the fluid flowing through the flow channel 20 is constant during the measurement of the object to be measured.
  • the microfluidic device 2 sequentially flows a plurality of cells through the flow channel 20, but the number of cells passing through at one time during the measurement of the object to be measured is one.
  • the cells C are an example of the object to be measured. Note that the object to be measured is not limited to the cells C, and may be, for example, a spheroid (cell mass), a microparticle, or a microorganism.
  • microparticles examples include pollen, microplastics, lipid microparticles, or polymer microparticles (e.g., particles for calibrating a flow cytometer).
  • microorganisms examples include bacteria, fungi, viruses, protozoa, etc.
  • the figure appropriately shows an xyz coordinate system as a three-dimensional Cartesian coordinate system.
  • the x-axis direction is the width direction of the flow channel 20.
  • the y-axis direction is the length direction of the flow channel 20.
  • the z-axis direction is a direction perpendicular to the flow channel 20, and is the depth direction of the flow channel 20.
  • the depth direction of the flow channel 20 is also called the height direction of the flow channel 20.
  • the flow of liquid in the flow channel 20 moves the cells C in the +y direction, which is the y-axis direction. That is, the cells C move along with the flow of the fluid in the length direction of the flow channel 20.
  • the width direction of the flow channel 20 or the depth direction of the flow channel 20 is, in other words, the direction perpendicular to the flow line of the fluid flowing along with the cells C.
  • the width and depth of the flow path 20 are equal.
  • the cross section of the flow path 20 is square.
  • the width and depth of the flow path 20 may be different.
  • the cross section of the flow path 20 may be rectangular.
  • the light source 3 and the spatial light modulation unit 4 function to irradiate the flow channel 20 with structured illumination light SLE as described below.
  • the light source 3 irradiates illumination light LE toward the flow path 20.
  • the illumination light LE is converted into light having a specific illumination pattern (structured illumination light SLE) through the spatial light modulation unit 4. That is, the illumination light LE structured by the spatial light modulation unit 4 is irradiated to the flow path 20 as the structured illumination light SLE.
  • the illumination light LE emitted by the light source 3 may be coherent light or incoherent light.
  • An example of coherent light is laser light
  • an example of incoherent light is LED (light-emitting diode) light.
  • the illumination light LE emitted by the light source 3 is, for example, coherent light.
  • the spatial light modulation unit 4 is disposed on the optical path between the light source 3 and the flow path 20.
  • This arrangement is also referred to as a structured illumination configuration.
  • Structured illumination is a configuration for irradiating illumination light LE as structured illumination light SLE that has been structured by, for example, the spatial light modulation unit 4.
  • the structured illumination light SLE is imaged as a structured illumination pattern 21 at the optical position where the structured illumination light SLE is irradiated in the flow path 20.
  • the optical position in the flow path 20 where the structured illumination light SLE is irradiated is also referred to as the irradiation position. Details of the structured illumination pattern 21 will be described later.
  • the spatial light modulation unit 4 changes the optical characteristics of the irradiated light (illumination light LE in the example of FIG. 1) and converts it into light having a specific illumination pattern (in the example of FIG. 1, converting it into light that is irradiated to the flow path 20 as structured illumination light SLE) is also referred to as structuring.
  • the illumination pattern is also referred to as a bright spot pattern.
  • the spatial light modulation unit 4 is disposed on the optical path from the light source 3 to the flow path 20, but it can also be disposed on the optical path between the flow path 20 and the photodetector 6 as described in the second embodiment below.
  • the spatial light modulation unit 4 is an optical system that is disposed on the optical path between the light source 3 and the photodetector 6 to structure light (illumination light LE in this embodiment, and signal light LSa from the cell C in the second embodiment below).
  • FIG. 2 is a diagram showing an example of the configuration of the spatial light modulation unit 4 according to this embodiment.
  • the spatial light modulation unit 4 includes a spatial modulator 40, a first lens 41, a spatial filter 42, a second lens 43, and an objective lens 44.
  • the spatial modulator 40, the first lens 41, the spatial filter 42, the second lens 43, and the objective lens 44 of the spatial light modulation unit 4 are arranged on the optical path between the light source 3 and the flow path 20 in this order from the side closest to the light source 3.
  • the spatial modulator 40 has multiple regions with different optical characteristics.
  • the spatial modulator 40 can change the optical characteristics of the incident light for each of the multiple regions included in the incident surface of the incident light. This allows the spatial modulator 40 to change (modulate) the characteristics of the light from the light source 3 for each of the multiple regions.
  • the spatial modulator 40 performs modulation to change the optical characteristics of the transmitted illumination light LE to different states in two or more of the multiple regions that make up the spatial modulator 40.
  • the optical characteristics of the illumination light LE are modulated for each of the multiple regions that the spatial modulator 40 has, and a bright spot pattern is imparted to the illumination light LE according to the distribution of the multiple regions. Details of the bright spot pattern will be described later.
  • the optical characteristics of the illumination light LE are, for example, characteristics related to one or more of the intensity, wavelength, phase, and polarization state of light, but are not limited to these.
  • the optical characteristics that change by passing through the spatial light modulator 40 are not limited to one, and two or more optical characteristics of the illumination light LE may change simultaneously by passing through the spatial light modulator 40.
  • the spatial modulator 40 is, for example, a diffractive optical element (DOE), a spatial light modulator (SLM), or a digital mirror device (DMD).
  • the spatial modulator 40 is preferably a DMD.
  • the spatial modulator 40 is, for example, a DOE.
  • the bright spot pattern does not change over time, at least during measurement by the flow cytometer 1.
  • the optical characteristics of the multiple regions of the spatial modulator 40 do not change over time, at least during measurement of the measurement object by the flow cytometer 1.
  • the optical characteristics of the multiple regions of the spatial modulator 40 may be changed, for example, by being controlled by the PC 8. Even in this case, the optical characteristics of the multiple regions of the spatial modulator 40 are changed in advance by the PC 8 before the start of measurement by the flow cytometer 1, and do not change over time while the flow cytometer 1 is measuring the object to be measured.
  • the measurement object is irradiated with the structured illumination light SLE modulated by the spatial modulator 40 at the irradiation position.
  • How the structured illumination pattern 21 at the irradiation position is formed at the irradiation position is determined by how the light passing through each of the multiple regions constituting the spatial modulator 40 is modulated.
  • the structured illumination light SLE can be configured such that the intensity of the illumination light is modulated by the spatial light modulator, and only the illumination light irradiated to a certain region among the multiple regions of the spatial light modulator 40 is irradiated to the irradiation position at the irradiation position of the flow path 20, and the illumination light irradiated to a position other than the region is not irradiated to the irradiation position.
  • the structured illumination pattern 21 is formed as a binary pattern defined by the irradiation/non-irradiation of the illumination light at the irradiation position of the flow path 20. In the following description, an example is described in which the structured illumination pattern 21 is formed as a binary pattern defined by the irradiation/non-irradiation of the illumination light at the irradiation position of the flow path 20, but is not limited to this.
  • the area of the smallest unit (hereinafter, also referred to as a bright spot or light spot) constituting the structured illumination pattern 21 shows a three-dimensional shape.
  • the area is irradiated as structured illumination light SLE having a pattern composed of, for example, a plurality of squares of equal size.
  • the fluorescent molecules (including the case where the cell C is labeled with a fluorescent molecule) contained in the cell C passing through the irradiation position are excited by the structured illumination light SLE and emit light. This emission (i.e., fluorescence) is an example of signal light LS emitted from the cell C.
  • the signal light LS examples include transmitted light in which the structured illumination light SLE has passed through the cell C, scattered light in which the structured illumination light SLE has been scattered by the cell C, and interference light between light transmitted or scattered by the structured illumination light SLE through the cell C and other light that has not passed through or been scattered by the cell C. Note that the above is an example, and the signal light LS is not limited to these.
  • the light spot which is the smallest unit constituting the structured illumination pattern 21, can be cubic, cylindrical, or spherical.
  • the shape of the light spot constituting the structured illumination pattern 21 is a square, circle, rectangle, or ellipse.
  • the size of the smallest unit area constituting the structured illumination pattern 21 can be freely changed, but it is preferable to set it to be sufficiently small compared to the size of the measurement object (e.g., cell C) or the size of the internal structure to be observed.
  • the measurement object when the measurement object is a normal-sized cell, it is preferable to set the smallest unit area constituting the structured illumination pattern 21 as a circle with a diameter of about 400 nm to 1 ⁇ m on the plane where the structured illumination pattern 21 of the flow path 20 is focused.
  • the structured illumination pattern 21 can be set to a configuration in which 500 to 1000 light spots are randomly distributed.
  • the first lens 41 focuses the structured illumination light SLE transmitted through the spatial modulator 40 onto the spatial filter 42 .
  • the spatial filter 42 removes light that is unnecessary for the structured illumination pattern 21 at the irradiation position from the structured illumination light SLE focused by the first lens 41 .
  • the second lens 43 converts the structured illumination light SLE from which unnecessary light has been removed by the spatial filter 42 into parallel light.
  • the objective lens 44 collects the structured illumination light SLE collimated by the second lens 43 and forms an image at the irradiation position of the flow channel 20.
  • the structured illumination light SLE transmitted through the objective lens 44 is irradiated onto the cell C at the irradiation position of the flow channel 20.
  • the objective lens 44 may be a dry objective lens or an immersion objective lens, such as an oil immersion lens or a water immersion lens.
  • the light detection optical system 5 includes an imaging optical system that forms an image of the cell C on the photodetector 6.
  • the signal light LS from the cell C is collected by an imaging lens included in this imaging optical system, and an image of the cell C is formed at the position of the photodetector 6.
  • the signal light LS from the cell C is fluorescence, transmitted light, scattered light, interference light, or the like.
  • the imaging lens included in the imaging optical system is disposed at a position where the imaging lens is focused on the irradiation position of the flow channel 20.
  • the light detection optical system 5 may further include a dichroic mirror and a wavelength selective filter in addition to the imaging optical system.
  • the photodetector 6 detects the image of the cell C formed by the signal light LS through the imaging optical system.
  • the photodetector 6 detects the optical signal and converts it into an electrical signal.
  • One example of the photodetector 6 is a photomultiplier tube (PMT).
  • the photodetector 6 detects the optical signal in a time series.
  • the photodetector 6 may be a single sensor composed of a single photodetection element, or may be a multi-sensor composed of multiple photodetection elements. When the photodetector 6 is a multi-sensor, the photodetector 6 is, for example, a multi-anode PMT.
  • the light detection optical system 5 and the photodetector 6 are arranged, for example, on the upper side of the flow path 20 (+z direction in the z-axis direction).
  • the photodetector 6 may be arranged at a position other than the upper side of the flow path 20.
  • the photodetector 6 may be arranged on the right side (+x-axis direction), left side (-x-axis direction), or lower side (-z direction in the z-axis direction) of the flow path 20.
  • the photodetector 6 can be arranged at one or more positions in the lateral direction (the direction of the side of the flow path 20; i.e., the +x or -x direction from the flow path 20) or the front-rear direction (the direction of the upper and lower surfaces of the flow path 20; i.e., the +z or -z direction from the flow path 20) with respect to the direction in which the structured illumination light SLE is incident on the irradiation position of the flow path 20.
  • multiple photodetectors 6 by arranging multiple photodetectors 6 at different positions, multiple different signal light LS (e.g., fluorescence and scattered light) can be detected individually.
  • the light detection optical system 5 is disposed on the optical path between the flow path 20 and the photodetector 6 in a direction in which the photodetector 6 faces the flow path 20.
  • the photodetector 6 is disposed on the right side (positive x-axis direction) of the flow path 20
  • the light detection optical system 5 is disposed on the right side (positive x-axis direction) of the flow path 20, on the optical path between the flow path 20 and the photodetector 6.
  • the DAQ device 7 converts the electrical signal waveforms output by the photodetector 6 into electronic data for each waveform.
  • the electronic data includes a combination of time and the intensity of the electrical signal.
  • One example of the DAQ device 7 is an oscilloscope.
  • the PC8 generates optical information as morphological information of the cell C based on the electronic data output from the DAQ device 7.
  • the morphological information is information that indicates one or more of the morphology and structure of the cell.
  • the PC8 also stores the optical information that it generates.
  • the optical information is, for example, information in which a time series change in the intensity of the optical signal, which is the intensity of the signal light LS from the cell C, is shown as a waveform.
  • This waveform signal corresponds to the morphology and structure of the cell C, and the optical information can be used to identify the cell C.
  • the optical information is used, for example, in machine learning, as training data for learning the relationship between the morphology of the target cell and the waveform signal.
  • a prediction model (trained model) created using the optical information as training data makes it possible to distinguish the target cell from cells other than the target cell from the waveform signal when a sample containing a specimen other than the target cell is measured.
  • the PC 8 is an example of an information generating device that generates optical information indicating the form or structure of the measurement object based on the intensity of the optical signal detected by the detector.
  • a measurement method is called a dynamic ghost imaging (GMI) method, which is a method of irradiating a moving measurement object with illumination light and detecting modulated light from the measurement object irradiated with the illumination light, and detects morphological information of the measurement object as optical information by adding a bright spot pattern to either the illumination light or the modulated light.
  • the morphological information of the observation object can be extracted as optical information having high resolution by experiencing a bright spot pattern having a length of a predetermined degree or more.
  • the waveform of the time-series change in the optical signal intensity acquired by the GMI method contains the morphological information of the cell C with high resolution.
  • the shape of the waveform of the waveform signal corresponds to the morphology and structure of the cell C, and the optical information acquired by the GMI method can be used to identify the cell based on its morphology and structure.
  • the spatial modulator 40 is an optical element that imparts a bright spot pattern (structured illumination pattern) to illumination light when the GMI method is applied in a structured illumination configuration.
  • FIG. 3 is a diagram showing an example of the structured illumination pattern 21 according to the present embodiment.
  • Fig. 4 is a diagram showing an example of a Fourier base used to generate partial patterns constituting the structured illumination pattern 21. Note that in each of Figs. 3 and 4, the position in the length direction (y-axis direction) of the flow channel 20 is indicated by the number of pixels.
  • the structural illumination pattern 21 is composed of six partial patterns arranged at different positions in the direction in which the object to be measured moves (Y-axis direction).
  • a partial pattern is an illumination pattern for a portion of an area included in the structural illumination pattern 21.
  • the structural illumination pattern 21 is composed of partial patterns of equal size (area).
  • the structural illumination pattern 21 is composed of at least two partial patterns. The partial patterns are arranged at different positions in the entire structural illumination pattern without overlapping with each other.
  • the structured illumination pattern 21 is composed of six partial patterns: partial pattern P1, partial pattern P2, partial pattern P3, partial pattern P4, partial pattern P5, and partial pattern P6.
  • Each of the six partial patterns from partial pattern P1 to partial pattern P6 is a pattern generated based on a Fourier basis.
  • all six partial patterns constituting the structured illumination pattern 21 are patterns generated based on the Fourier basis, but this is not limited to the above.
  • the partial patterns included in the structured illumination pattern 21 may include partial patterns that are not based on the Fourier basis.
  • a pattern that is not based on the Fourier basis is, for example, a random pattern that has been used in the conventional GMI method. Therefore, in the structured illumination pattern 21, as long as at least one of the partial patterns included in the structured illumination pattern 21 is a pattern based on the Fourier basis, a part of the structured illumination pattern 21 may include a partial pattern that is not based on the Fourier basis.
  • FIG. 4 shows six Fourier bases: Fourier basis F1, Fourier basis F2, Fourier basis F3, Fourier basis F4, Fourier basis F5, and Fourier basis F6.
  • Each of the six Fourier bases, Fourier basis F1 to Fourier basis F6, is used to generate six partial patterns that make up the structured illumination pattern 21.
  • the partial patterns constituting the structured illumination pattern 21 are composed only of patterns generated based on a Fourier basis, but this is not limited to the above.
  • the partial patterns included in the structured illumination pattern 21 may include areas not based on a Fourier basis, as long as at least 50% or more of the partial pattern contains a pattern generated based on a Fourier basis.
  • a pattern not based on a Fourier basis is, for example, a random pattern that has been used in the conventional GMI method.
  • a partial pattern may be created by cutting out a pattern generated based on a Fourier basis and arranging it in a random pattern.
  • the partial pattern is also called a pattern generated based on a Fourier basis.
  • the Fourier basis is specified by a pair of spatial frequencies in the x-axis direction and y-axis direction.
  • these pairs of spatial frequencies in the x-axis direction and y-axis direction are simply called frequencies (or modes). Therefore, the Fourier basis is specified by frequencies (modes).
  • the pattern based on the Fourier basis may be a pattern based on one type of Fourier basis, or may be a pattern in which patterns based on multiple Fourier bases are superimposed.
  • a pattern based on a Fourier basis is a pattern in which the pattern is represented by Fourier bases of a predetermined number or less types of frequencies (modes).
  • the predetermined number is, for example, any number between 1 and 10.
  • the predetermined number may be 1, 2, or 3. If a certain pattern includes patterns based on many Fourier bases, the pattern will no longer differ from the bright spot patterns conventionally used in the GMI method. For this reason, in this embodiment, the pattern based on the Fourier base is a pattern in which the pattern is represented by Fourier bases of a predetermined number or less types of frequencies (modes).
  • the structured illumination pattern 21 may be patterns based on the Fourier basis and different from each other.
  • the structured illumination pattern 21 includes a combination of a first partial pattern having a high spatial frequency based on the Fourier basis and a second partial pattern having a low spatial frequency.
  • the combination may be performed under the condition that the difference between the spatial frequency of the first partial pattern and the spatial frequency of the second partial pattern is greater than a predetermined value.
  • the combination may be performed under the condition that the difference between the spatial frequency of the first partial pattern and the spatial frequency of the second partial pattern is smaller than a predetermined value.
  • the partial patterns included in the structured illumination pattern 21 are different patterns based on a Fourier basis, compared to a case in which the structured illumination pattern 21 includes a partial pattern based on one type of Fourier basis. The method of optimizing the partial patterns will be described later.
  • At least two partial patterns based on the Fourier basis are arranged adjacent to each other in the direction in which the measurement object moves. Note that a partial pattern that is not based on a Fourier basis may be arranged between the at least two partial patterns based on a Fourier basis.
  • the vertical size of the partial patterns that make up the structural illumination pattern 21 is equal to the vertical size of the structural illumination pattern 21.
  • the size in the y-axis direction (horizontal size) of the partial patterns that make up the structural illumination pattern 21 is equal to the size in the x-axis direction (vertical size).
  • the shape of the partial patterns is a square.
  • the size in the y-axis direction of the partial patterns constituting the structured illumination pattern 21 is equal to the size in the x-axis direction, but this is not limited to the above.
  • the size in the y-axis direction of the partial patterns constituting the structured illumination pattern 21 may be different from the size in the x-axis direction.
  • the shape of the partial patterns may be rectangular.
  • the structural illumination pattern 21 shown in FIG. 3 an example in which the structural illumination pattern 21 is composed of six partial patterns has been described, but this is not limited to this.
  • the number of partial patterns that compose the structural illumination pattern 21 may be two or more.
  • the number of partial patterns that compose the structural illumination pattern 21 can be three or more and eight or less.
  • the number of partial patterns constituting the structural illumination pattern 21 and their size in the y-axis direction are determined in advance according to the size of the structural illumination pattern 21 in the y-axis direction.
  • the number of partial patterns constituting the structural illumination pattern 21 is set to two or more, so that the size in the y-axis direction (horizontal size) of the partial patterns constituting the structural illumination pattern 21 is smaller than half the size of the structural illumination pattern 21. It is also desirable that the size of the partial patterns in the y-axis direction is greater than half the size in the x-axis direction.
  • adjacent partial patterns may be arranged with overlapping portions.
  • a first partial pattern and a second partial pattern may be arranged with overlapping portions.
  • the overlapping portion of the first partial pattern and the second partial pattern may be interpreted as a new partial pattern.
  • it is the same as when a pattern in which the first partial pattern is shorter than the original size by the overlapping portion, a new partial pattern which is the overlapping portion, and a pattern in which the second partial pattern is shorter than the original size by the overlapping portion are arranged adjacent to each other in this order.
  • FIG. 5 is a diagram showing an example of the configuration of the information generating device 10 according to the present embodiment.
  • the information generating device 10 includes a control unit 11 and a storage unit 12.
  • the control unit 11 is equipped with, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or an FPGA (field-programmable gate array), and performs various calculations and information transmission and reception.
  • the control unit 11 is equipped with a signal intensity acquisition unit 110, an optical information generation unit 111, a bright spot pattern generation unit 112, and a combination optimization unit 113.
  • the signal intensity acquisition unit 110, the optical information generation unit 111, the bright spot pattern generation unit 112, and the combination optimization unit 113 are each realized, for example, by the CPU loading a program read from a ROM (Read Only Memory) into a RAM (Random Access Memory) and executing processing according to the program.
  • the ROM and RAM are included in the memory unit 12.
  • the signal strength acquisition unit 110 acquires the electronic data output from the DAQ device 7.
  • the optical information generating unit 111 generates optical information based on the electrical signal waveform indicated by the electronic data acquired by the signal intensity acquiring unit 110.
  • the optical information is an electrical signal waveform formed by a combination of the time and the intensity of the electrical signal contained in the electronic data.
  • the optical information includes morphological information of the cell C.
  • the bright spot pattern generating unit 112 generates a bright spot pattern.
  • the bright spot pattern is generated and expressed as a binary matrix.
  • the combination optimization unit 113 performs a combination optimization process.
  • the combination optimization process is a process for designing a structured illumination pattern 21 by combining a plurality of partial patterns to improve the classification accuracy of the measurement object.
  • the storage unit 12 stores various types of information.
  • the information stored in the storage unit 12 includes optical information 120 and bright spot pattern information 121.
  • the storage unit 12 is configured using a storage device such as a magnetic hard disk device or a semiconductor storage device.
  • the bright spot pattern generating unit 112 and the combination optimization unit 113 may be provided in an information generating device separate from the information generating device 10.
  • the information generating device is, for example, a PC or a server.
  • FIG. 6 is a diagram showing an example of the bright spot pattern generation process according to the present embodiment.
  • FIGS. 7 to 9 are diagrams that explain the process of generating a bright spot pattern according to the generation process shown in FIG. 6. Note that, although FIGS. 7 to 9 show the stage before the bright spot pattern is arranged as a partial pattern of the structured illumination pattern 21, an xyz coordinate system is shown as in the other diagrams to show the correspondence with the direction when the bright spot pattern is arranged as a partial pattern of the structured illumination pattern 21.
  • Step S10 The bright spot pattern generating unit 112 acquires a two-dimensional image indicating the Fourier basis.
  • the bright spot pattern generating unit 112 acquires the two-dimensional image indicating the Fourier basis, for example, by generating a two-dimensional image indicating the Fourier basis by itself.
  • the bright spot pattern generating unit 112 may acquire the two-dimensional image indicating the Fourier basis from an external information processing device.
  • FIG. 7 is a diagram showing an example of a Fourier basis according to this embodiment.
  • the Fourier basis is shown as a two-dimensional image.
  • 16 Fourier bases are shown as an example.
  • Step S20 The bright spot pattern generating unit 112 determines a bright spot arrangement area in the two-dimensional image representing the Fourier basis.
  • a bright spot arrangement area is a collection of positions (i.e., pixels) in the two-dimensional image where bright spots can be arranged. In other words, bright spots are not arranged in areas of the two-dimensional image other than the bright spot arrangement area.
  • FIG. 8 shows an example of a bright spot arrangement area determined based on the Fourier basis shown in FIG. 7.
  • the area inside boundary B1 is determined as the bright spot arrangement area.
  • the area inside boundary B21 and the area inside boundary B22 are each determined as the bright spot arrangement area.
  • the bright spot arrangement area is determined, for example, based on pixel values in a two-dimensional image showing the Fourier basis.
  • an area in the two-dimensional image showing the Fourier basis where the pixel value ranking is equal to or higher than a predetermined ranking is determined as the bright spot arrangement area.
  • the ranking of pixel values refers to the ranking of each pixel constituting the two-dimensional image showing the Fourier basis when the pixel values are arranged from the largest to the smallest.
  • the predetermined ranking is, for example, a ranking corresponding to the top 20 percent. Note that the predetermined ranking may be a ranking other than the top 20 percent.
  • a region in which the pixel values in the two-dimensional image showing the Fourier base are ranked at or below a predetermined rank may be determined as a bright spot arrangement region.
  • the predetermined rank is, for example, a rank corresponding to the bottom 20 percent.
  • a region in a two-dimensional image showing the Fourier basis whose pixel values are equal to or greater than a predetermined pixel value may be determined as a bright spot arrangement region, regardless of the order of pixel values.
  • a region in a two-dimensional image showing the Fourier basis whose pixel values are equal to or less than a predetermined pixel value may be determined as a bright spot arrangement region.
  • Step S30 The bright spot pattern generating unit 112 places a bright spot pattern in the bright spot arrangement region.
  • FIG. 9 shows an example of a bright spot pattern placed in the bright spot arrangement region.
  • a predetermined percentage of pixels in the bright spot arrangement region are selected as pixels for placing bright spots based on pixel values in a two-dimensional image (FIG. 7) showing the Fourier basis.
  • each pixel included in the bright spot arrangement region is weighted based on the pixel value in the two-dimensional image that represents the Fourier basis.
  • the larger the pixel value the larger the weight is assigned. Note that, if an area in which the pixel value is ranked below a predetermined rank in step S20 is determined to be a bright spot arrangement region, the smaller the pixel value, the larger the weight is assigned.
  • a predetermined percentage of pixels is selected by weight-based sampling.
  • This predetermined percentage is, for example, 5 percent.
  • the predetermined percentage may be a percentage other than 5 percent.
  • weight-based sampling for example, a score is assigned to each pixel included in the bright spot arrangement area, with the probability that the larger the weight, the larger the value, and a predetermined percentage of pixels are selected in descending order of score.
  • pixels with a score equal to or greater than a predetermined value may be selected, in which case the proportion of pixels selected in the bright spot arrangement region is not predetermined. It is also possible to randomly select a predetermined percentage of pixels in the bright spot arrangement area without using weights.
  • a bright spot arrangement area is determined in a two-dimensional image representing the Fourier base, and then a bright spot pattern is arranged in the bright spot arrangement area, but this is not limiting.
  • the process of determining the bright spot arrangement area may be omitted.
  • a bright spot pattern may be arranged in the two-dimensional image representing the Fourier base.
  • the entire two-dimensional image representing the Fourier base may be set as the bright spot arrangement area, and a bright spot pattern may be arranged based on the method shown in step S30.
  • the method of generating the bright spot pattern is not limited to the bright spot pattern generation process shown in FIG. 6.
  • a method of generating a bright spot pattern first, bright spots may be randomly arranged, and then a process of deleting bright spots from the bright spots based on the Fourier basis, or a process of adding new bright spots to the bright spots may be performed.
  • a bright spot pattern based on the Fourier basis is generated by gradually making the bright spot pattern resemble the bright spot pattern based on the Fourier basis.
  • a bright spot is arranged at a certain position is determined, for example, based on a probability based on pixel values in a two-dimensional image showing the Fourier basis for that position.
  • bright spots may be deleted in advance from areas that are not included in the bright spot arrangement area as shown in FIG. 8.
  • Whether or not a certain partial pattern is a pattern based on a Fourier basis can be determined by the ratio of frequency components that are clearly contained more than other frequency components to the total frequency components when the partial pattern is decomposed by Fourier transform.
  • the frequency components that are included in a larger amount than the other frequency components are those whose intensity is equal to or greater than a threshold value, the threshold value being different for each frequency component.
  • the threshold value is determined, for example, by using a random pattern. A large number of random patterns are created that are the same size as the pattern of the portion to be determined and have the same bright spot rate. The threshold value is determined based on the intensity of each frequency component when the created random pattern is decomposed into frequency components by performing a Fourier transform. It is desirable to create 100 or more random patterns.
  • the bright spot rate is the ratio of pixels that have bright spots to the total number of pixels.
  • the threshold value for a certain frequency component can be determined, for example, by finding the intensity of that frequency component for each random pattern created and calculating a statistical value for all the random patterns created.
  • the statistical value can be determined, for example, as the sum of the average intensity of that frequency component and a value obtained by multiplying the standard deviation by a certain constant.
  • An appropriate value for the constant is selected according to the pre-processing (smoothing process, etc.) performed before the Fourier transform, the size of the pattern, and the bright spot rate.
  • the range of the constant value is, for example, 0.1 to 2.0.
  • the frequency components of the pattern of the part to be judged are discretized and expressed as half the number of pixels.
  • the discretized expression is made, if there are frequency components above a threshold among the frequency components, and the proportion of frequency components above the threshold is, for example, 25% or less, then the part pattern can be judged to be a pattern based on the Fourier basis in this embodiment.
  • the partial pattern can be decomposed into frequency components by Fourier transform, and a similar determination can be made based on whether the proportion of frequencies with an intensity equal to or greater than a threshold is equal to or less than a predetermined number.
  • the structured illumination pattern 21 is composed of multiple square bright spots of equal size, and therefore the partial patterns are also composed of multiple square bright spots of equal size. Therefore, when the proportion of the Fourier base contained in the partial pattern is determined by Fourier transform, peaks corresponding to spatial frequencies higher than the spatial frequency of the original Fourier base are included as noise.
  • peaks smaller than a predetermined size may be excluded in advance as noise before the determination is made.
  • the partial pattern made up of square bright spots may be converted into a continuous pattern before the Fourier transform is performed.
  • a smooth function that indicates the density of the bright spots for each position is found, and the pattern indicated by the value of this function is treated as a continuous pattern.
  • step S30 in order to determine whether the bright spot pattern obtained in step S30 is a bright spot pattern generated based on the bright spot arrangement area in step S20, for example, it is sufficient to determine the boundary of the area where the bright spots are dense and determine whether the determined boundary and the bright spot arrangement area match.
  • whether the bright spot pattern obtained in step S30 is a bright spot pattern generated based on the bright spot arrangement area in step S20 may be determined by determining the boundary of the area where the bright spots are dense and determining whether the determined boundary and the bright spot arrangement area match. In these determinations, it is not necessary for the determined boundary and the bright spot arrangement area to match exactly; if they match approximately, it may be determined that the bright spot pattern was generated based on the bright spot arrangement area.
  • step S20 determines whether the bright spot arrangement area obtained in step S20 has been determined based on a Fourier basis. For example, when multiple bright spot arrangement areas are determined from Fourier bases in which a predetermined order of pixel values is changed in various ways in a manner similar to the processing of step S20, it is possible to determine whether any of the multiple bright spot arrangement areas matches the bright spot arrangement area being determined. In this determination, it is not necessary for the bright spot arrangement areas to match exactly; if they match approximately, it may be determined that the bright spot arrangement area has been determined based on a Fourier basis.
  • the combination optimization method is a method for improving the classification accuracy of a measurement object by combining a plurality of bright spot patterns.
  • Fig. 10 is a diagram showing an example of the combination optimization process according to the present embodiment.
  • the combination optimization process shown in Fig. 10 is executed by the bright spot pattern generation unit 112 and the combination optimization unit 113 provided in the information generating device 10.
  • the combination optimization method is executed by the combination optimization process shown in Fig. 10.
  • FIGS. 11 to 17 are diagrams that explain the process of generating a structured illumination pattern 21 according to the bright spot pattern optimization method shown in FIG. 10. Note that, although FIGS. 11 to 17 show the stage before the bright spot pattern is arranged as a partial pattern of the structured illumination pattern 21, an xyz coordinate system is shown as in the other diagrams to show the correspondence with the direction when the bright spot pattern is arranged as a partial pattern of the structured illumination pattern 21.
  • Step S110 The combination optimization unit 113 causes the bright spot pattern generation unit 112 to generate a bright spot pattern.
  • the combination optimization unit 113 causes the bright spot pattern generated by the bright spot pattern generation unit 112 to be stored in the storage unit 12 as bright spot pattern information 121.
  • the bright spot pattern generating unit 112 generates a bright spot pattern based on the Fourier basis by sampling multiple times for each frequency.
  • the bright spot pattern is generated, for example, based on the bright spot pattern generating process shown in FIG. 6.
  • Figures 11 and 12 show two-dimensional images showing the Fourier bases.
  • 64 Fourier bases are shown as an example. Note that the upper left Fourier base corresponding to the zero mode is not used to generate the bright spot pattern.
  • 49 Fourier bases are shown as an example.
  • a total of 112 Fourier bases shown in Figures 11 and 12 are used to generate the bright spot pattern as an example.
  • Fourier bases shown in Figures 11 and 12 may be used as the Fourier bases.
  • a Fourier base having a higher spatial frequency than the Fourier bases shown in Figures 11 and 12 may be used as the Fourier bases.
  • a bright spot arrangement area is determined as described in step S20 ( Figure 6).
  • two types of bright spot arrangement areas are determined: an area where the pixel value ranking is equal to or higher than a predetermined rank, and an area where the pixel value ranking is equal to or lower than a predetermined rank. Therefore, two types of bright spot arrangement areas are determined for each of the 112 Fourier bases. In other words, a total of 112 x 2 bright spot arrangement areas are determined.
  • step S30 (FIG. 6)
  • sampling is used to place bright spot patterns in the bright spot arrangement regions.
  • sampling is performed multiple times in the bright spot arrangement regions for each frequency (Fourier base). As an example, the number of sampling times is 16 times. Therefore, 16 samplings are performed for each of the 112 ⁇ 2 bright spot arrangement regions, and a total of 112 ⁇ 2 ⁇ 16 different bright spot patterns are generated.
  • FIGS. 13 and 14 show an example of a bright spot pattern generated by performing a certain sampling on the bright spot arrangement area for each Fourier base. Note that, of two types of bright spot arrangement areas, namely bright spot arrangement areas determined as areas where the pixel value ranking is equal to or higher than a predetermined rank, and bright spot arrangement areas determined as areas where the pixel value ranking is equal to or lower than a predetermined rank, FIGS. 13 and 14 show the result of performing sampling on the former bright spot arrangement area determined as an area where the pixel value ranking is equal to or higher than a predetermined rank.
  • Step S120 The combination optimization unit 113 determines the frequency with the highest classification accuracy.
  • the classification accuracy of each frequency is evaluated based on a statistical value of the classification accuracy of the bright spot pattern obtained by multiple samplings.
  • the statistical value is the median. Note that other statistical values, such as the average value, may also be used as the statistical value.
  • step S120 for each frequency and each of the two types of bright spot arrangement regions, the classification accuracy is evaluated as the median value of the 16 samplings.
  • the bright spot pattern with the highest classification accuracy is determined from the 112 x 2 bright spot patterns.
  • cross-validation is used to evaluate the classification accuracy of each bright spot pattern.
  • images of multiple cells captured in advance are used.
  • the optical information of the cell when a certain bright spot pattern for which classification accuracy is to be evaluated is used as a structured illumination pattern is calculated for each of the images of the multiple cells.
  • the optical information calculated for each of the images of the multiple cells can be used as the optical information of the cell when the bright spot pattern for which classification accuracy is to be evaluated is used as a structured illumination pattern.
  • a part of the calculated optical information is used as learning data for creating a model, and a trained model (classifier) for cell classification that infers the type of cell from the optical information is created.
  • the remaining part of the calculated optical information can be used to evaluate the classification accuracy when the bright spot pattern for which classification accuracy is to be evaluated is used as a structured illumination pattern.
  • the optical information used to evaluate the classification accuracy is determined using a trained model that infers the type of cell from optical information, and the classification accuracy can be evaluated by comparing it with the correct answer for which cell image the optical information is actually obtained from.
  • a support vector machine (SVM) is used for the machine learning.
  • SVM support vector machine
  • a machine learning algorithm other than SVM may also be used for the machine learning.
  • FIG. 15 shows bright spot pattern P14 as an example of the bright spot pattern of the frequency determined to have the highest classification accuracy. Note that for the purposes of explanation, FIG. 15 shows only five bright spot patterns (bright spot pattern P11, bright spot pattern P12, bright spot pattern P13, bright spot pattern P14, and bright spot pattern P15) out of the bright spot patterns corresponding to the 112 x 2 bright spot arrangement areas. Also, FIG. 15 shows only the results of one of the multiple samplings for each frequency.
  • Step S130 The combination optimization unit 113 determines the pattern with the highest classification accuracy among the bright spot patterns obtained by multiple sampling for the bright spot patterns of the frequency determined in step S120.
  • the classification accuracy of each bright spot pattern obtained by multiple sampling is evaluated in step S120.
  • the classification accuracy is compared between the bright spot patterns obtained by multiple sampling, thereby determining the pattern with the highest classification accuracy among the bright spot patterns obtained by multiple sampling.
  • Step S140 The combination optimization unit 113 adds the bright spot pattern determined in step S130 to the structured illumination pattern as a partial pattern.
  • the combination optimization unit 113 adds the bright spot pattern to be added as a partial pattern so that the bright spot pattern is positioned adjacent to the partial pattern to which it has already been added in the direction in which the measurement object moves.
  • the combination optimization unit 113 adds the new partial pattern adjacent to the partial pattern already added on the positive side in the y-axis direction.
  • Step S150 The combination optimization unit 113 determines whether a predetermined number of partial patterns have been added to the structural illumination pattern.
  • the predetermined number is, for example, six (see FIG. 3). Note that the predetermined number is determined according to the desired size of the structural illumination pattern.
  • the predetermined number may be a number other than six. In particular, the predetermined number may be two.
  • step S150 determines that a predetermined number of partial patterns have been added to the structural illumination pattern (step S150; YES). If the combination optimization unit 113 determines that a predetermined number of partial patterns have been added to the structural illumination pattern (step S150; YES), it ends the combination optimization process. On the other hand, if the combination optimization unit 113 determines that a predetermined number of partial patterns have not been added to the structural illumination pattern (step S150; NO), it executes each process from step S120 to step S150 again.
  • FIG. 16 shows an example of a bright spot pattern in which the bright spot pattern P14 added as a partial pattern in the first process is combined with each of the multiple bright spot patterns in the second process. Note that, in FIG. 16, only five bright spot patterns (bright spot pattern P21, bright spot pattern P22, bright spot pattern P23, bright spot pattern P24, and bright spot pattern P25) among the bright spot patterns corresponding to the 112 ⁇ 2 bright spot arrangement areas are combined with the bright spot pattern P14 for the sake of explanation. In addition, in FIG.
  • the results of only one sampling among the multiple samplings for each frequency are shown for the five bright spot patterns combined with the bright spot pattern P14.
  • a bright spot pattern that combines a bright spot pattern P14 and a bright spot pattern P22 is determined to have the highest classification accuracy.
  • Fig. 17 shows an example of a bright spot pattern in which a bright spot pattern P14 is added as a partial pattern in the first process and a bright spot pattern P22 is added as a partial pattern in the second process in the third process, and each of the multiple bright spot patterns is combined.
  • Fig. 17 shows only five bright spot patterns (bright spot pattern P31, bright spot pattern P32, bright spot pattern P33, bright spot pattern P34, and bright spot pattern P35) among the bright spot patterns corresponding to the 112 x 2 bright spot arrangement areas, and a bright spot pattern in which the bright spot pattern P14 and the bright spot pattern P22 are combined for explanation.
  • Fig. 17 shows only the result of one sampling out of multiple samplings for each frequency for the five bright spot patterns combined with the bright spot pattern P14 and the bright spot pattern P22.
  • the combination optimization unit 113 ends the combination optimization process.
  • Bright spot patterns may be generated by a single sampling.
  • the process of step S130 is omitted in the combination optimization process shown in FIG. 10.
  • the bright spot pattern added as a partial pattern is a bright spot pattern generated based on a Fourier basis of a frequency determined to have high classification accuracy, but this is not limited to the above.
  • the bright spot pattern added as a partial pattern may be a bright spot pattern selected by a method other than the combinatorial optimization process.
  • the processes of step S120 and step S130 may be omitted at one time, and a bright spot pattern selected by a method other than the combinatorial optimization process may be added in step S140.
  • the bright spot pattern selected by a method other than the combinatorial optimization process is, for example, a random pattern.
  • the classification accuracy is evaluated for bright spot patterns in which the bright spot patterns are combined with partial patterns that have already been added; however, this is not limiting.
  • the classification accuracy of each bright spot pattern alone may be evaluated without combining the bright spot pattern with partial patterns that have already been added.
  • FIG. 18 An example of the cell images used for evaluating the classification accuracy is shown in Fig. 18.
  • a fluorescent image of a Raji cell and a fluorescent image of a Jurkat cell were used.
  • the size of the structured illumination pattern is 80 pixels in the x-axis direction and 480 pixels in the y-axis direction.
  • This structured illumination pattern is composed of a combination of six partial patterns.
  • the 112 Fourier bases shown in Figures 11 and 12 were used as the Fourier basis.
  • the proportion of the area in which the bright spots are located relative to the entire structured illumination pattern was set to 1 percent.
  • the classifier for evaluating classification accuracy was created using an SVM with the Nystroem method for kernel approximation.
  • Learning data optical information for learning
  • Inference data optical information for evaluation
  • Classification accuracy was evaluated based on the classification score (F-value) by comparing the inference result (cell classification result using the trained model) with the correct answer.
  • Fig. 10 The combinatorial optimization method shown in Fig. 10 was run multiple times by changing the random seed for the process of sampling bright spots from the Fourier bases, generating five structured illumination patterns.
  • Fig. 19 shows the combinations of Fourier bases used in the generated structured illumination patterns.
  • Fig. 20 shows an example of a bright spot pattern generated by sampling from the selected Fourier bases.
  • Figs. 19 and 20 also show the evaluation results for each of the five random seeds.
  • FIG. 21 is a diagram showing an example of the results of evaluating the classification accuracy of a structural illumination pattern obtained as a result of optimization according to this embodiment.
  • the results shown in FIG. 21 also show the classification accuracy of a structural illumination pattern obtained by optimizing combinations of random binary matrices using a similar process instead of partial patterns based on Fourier bases.
  • the number of random binary matrices used here was 112, the same number as the number of Fourier bases.
  • the structural illumination pattern was optimized using a method similar to the combinatorial optimization method shown in FIG. 10. Ten patterns of this structural illumination pattern were generated by changing the random seed, and the classification accuracy was evaluated for each of the ten patterns.
  • the classification accuracy when using a structured illumination pattern that combines partial patterns based on Fourier bases was higher than the classification accuracy when using a structured illumination pattern that combines a random binary matrix.
  • the optical element of this embodiment is an optical element that imparts a bright spot pattern in a measurement method that irradiates illumination light onto a moving object to be measured and detects modulated light from the object to be measured on which the illumination light is irradiated, and that detects morphological information of the object to be measured as optical information by imparting a bright spot pattern to either the illumination light or the modulated light.
  • the optical element has a plurality of regions with mutually differing optical properties, and the bright spot pattern (in this embodiment, structural illumination pattern 21) is imparted in accordance with the distribution of the plurality of regions.
  • the bright spot pattern (in this embodiment, structured illumination pattern 21) provided by the optical element includes at least two partial patterns (in this embodiment, partial pattern P1, partial pattern P2, partial pattern P3, partial pattern P4, partial pattern P5, and partial pattern P6) that are arranged at different positions in the direction in which the measurement object (in this embodiment, cell C) moves, and at least one of the partial patterns (in this embodiment, partial pattern P1, partial pattern P2, partial pattern P3, partial pattern P4, partial pattern P5, and partial pattern P6) is a pattern based on a Fourier basis.
  • the optical element according to this embodiment in this embodiment, spatial modulator 40
  • can impart to the illumination light a highly versatile bright spot pattern in this embodiment, structured illumination pattern 21
  • a highly versatile bright spot pattern in this embodiment, structured illumination pattern 21
  • the random patterns used as bright spot patterns sometimes made it difficult to capture image features (morphological features of the object being measured), resulting in insufficient classification accuracy. For example, it was difficult to capture image features such as stripes of a certain frequency or circular patterns the size of a cell with a random pattern, making it difficult to achieve high-precision classification of the object being measured.
  • the optical element (spatial modulator 40 in this embodiment) in the bright spot pattern (structured illumination pattern 21 in this embodiment) provided by the optical element (spatial modulator 40 in this embodiment), a pattern based on the Fourier basis is included as a partial pattern in part of the structured illumination pattern 21.
  • the optical element (spatial modulator 40 in this embodiment) according to this embodiment can detect image features that are difficult to capture with conventional random patterns, thereby improving classification accuracy for measurement objects of various sizes and shapes.
  • At least two of the partial patterns are patterns based on a Fourier basis and are different from each other.
  • the optical element according to this embodiment can include multiple patterns based on the Fourier basis in the bright spot pattern (in this embodiment, structured illumination pattern 21), which increases versatility in achieving high classification accuracy regardless of the size and shape of the object being measured, compared to when the bright spot pattern includes a partial pattern based on one type of Fourier basis.
  • the optical element of this embodiment in this embodiment, spatial modulator 40
  • at least two partial patterns based on the Fourier basis in this embodiment, partial pattern P1, partial pattern P2, partial pattern P3, partial pattern P4, partial pattern P5, and partial pattern P6 are arranged adjacent to each other in the direction in which the object to be measured (in this embodiment, cell C) moves.
  • the optical element according to this embodiment in this embodiment, the spatial modulator 40
  • the spatial modulator 40 can shorten the length of the bright spot pattern when the same frequency components are used, thereby improving the throughput of the flow cytometer 1.
  • the flow cytometer 1 also includes an optical element (in this embodiment, a spatial modulator 40), a light source 3, a microfluidic device 2, a photodetector 6, and an information generating device 10 (PC 8).
  • the light source 3 emits illumination light LE toward a measurement object (in this embodiment, a cell C).
  • the microfluidic device 2 includes a flow channel 20 through which the measurement object (cells C in this embodiment) can flow together with a fluid.
  • the photodetector 6 detects, in time series, the optical signal intensity, which is the intensity of modulated light (in this embodiment, signal light LS) emitted from the measurement object (in this embodiment, cell C).
  • the information generating device 10 (PC 8 ) generates optical information including morphological information of the measurement object (cell C in this embodiment) based on the optical signal intensity detected by the photodetector 6 .
  • the optical element (in this embodiment, the spatial modulator 40) is placed midway along the optical path from the light source 3 to the measurement object (in this embodiment, the cell C).
  • the object to be measured (in this embodiment, a cell C) is illuminated with illumination light (in this embodiment, structured illumination light SLE) to which a bright spot pattern (in this embodiment, structured illumination pattern 21) has been imparted by an optical element (in this embodiment, a spatial modulator 40).
  • the flow cytometer 1 can impart a highly versatile bright spot pattern (in this embodiment, the structured illumination pattern 21) to the illumination light, which achieves high classification accuracy regardless of the size and shape of the object being measured, thereby improving classification accuracy for objects of various sizes and shapes.
  • a highly versatile bright spot pattern in this embodiment, the structured illumination pattern 21
  • the bright spot pattern optimization device is a device that optimizes bright spot patterns in a measurement method in which illumination light is irradiated onto a moving measurement object, modulated light from the measurement object irradiated with the illumination light is detected, and morphological information of the measurement object is detected as optical information by adding a bright spot pattern to the illumination light.
  • the optimization device determines the bright spot pattern with the highest classification accuracy of the measurement object (in this embodiment, the cell image shown in FIG.
  • the bright spot pattern optimization device (the combination optimization unit 113 in this embodiment) can optimize the bright spot pattern so that the classification accuracy of the measurement object is high regardless of the size and shape of the measurement object, thereby improving the classification accuracy for a wide range of measurement objects.
  • a wide range of measurement objects means measurement objects having various sizes and shapes.
  • the bright spot pattern optimization device (in this embodiment, the combination optimization unit 113) can also optimize the bright spot pattern so as to increase the classification accuracy for a specific measurement object.
  • the illumination light is processed into structured illumination having a bright spot pattern (structured illumination pattern) by an optical element (DOE as an example) installed in the light path between the light source and the irradiation area, and the structured illumination pattern is irradiated onto the measurement object in the irradiation area of the flow path.
  • the structured illumination pattern which is a bright spot pattern, is configured as a binary pattern consisting of light-transmitting areas and light-blocking areas, for example.
  • modulated light modulated by the object to be measured is detected as structured detection that optically interacts with a bright spot pattern (modulation pattern) by an optical element (a mask, for example) installed in the optical path between the illumination region and the photodetector.
  • the illumination region of the flow path is irradiated with illumination light at a position optically conjugate with the position of the individual bright spots (formed as light-transmitting regions of a mask, for example) provided by the optical element, and the modulated light generated at that position is detected by the photodetector.
  • a bright spot pattern (modulation pattern) is provided by the arrangement of multiple regions with different optical characteristics (light-transmitting regions of a mask, for example) in the optical element installed in the optical path between the flow path and the photodetector.
  • the bright spot pattern therefore includes a structured illumination pattern and a modulation pattern.
  • the structured illumination pattern as shown in the structured illumination configuration of the first embodiment, is composed of multiple regions with different optical properties characterized by optical elements installed in the optical path between the illumination light source and the flow path.
  • the modulation pattern as shown in the structured detection configuration of the second embodiment below, is composed of multiple regions with different optical properties characterized by optical elements installed in the optical path between the flow path and the photodetector.
  • the second embodiment of the present invention will now be described in detail with reference to the drawings.
  • a configuration has been described in which the spatial light modulation unit 4 is disposed in the optical path between the light source and the flow path and modulates the illumination light LE.
  • the spatial light modulation unit is disposed in the optical path between the flow path and the photodetector and detects structured signal light.
  • the flow cytometer according to this embodiment is referred to as a flow cytometer 1a.
  • FIG. 22 is a diagram showing an example of the configuration of a flow cytometer 1a according to this embodiment.
  • the configuration of the flow cytometer 1a (Fig. 22) is similar to the configuration of the flow cytometer 1 (Fig. 1) except that the flow cytometer 1a (Fig. 22) includes a spatial light modulation unit 4a and an illumination optical system 9a instead of the spatial light modulation unit 4.
  • the spatial light modulation unit 4a includes a first lens 41a and a mask 40a. The first lens 41a and the mask 40a are arranged on the optical path between the flow path 20 and the photodetector 6 in this order from the side closer to the flow path 20.
  • the mask 40a is a spatial filter having light-transmitting regions formed by openings and light-opaque regions where light is blocked, that is, the mask 40a has a plurality of regions with different optical properties.
  • the mask 40a is provided at a position before the light detection optical system 5 and the light detector 6 on the optical path from the flow path 20 to the light detector 6. That is, the signal light LSa from the cell C is detected by the light detector 6 via the mask 40a and the light detection optical system 5.
  • a configuration in which the mask 40a is provided at a position between the flow path 20 and the light detector 6 on the optical path from the light source 3 to the light detector 6, as in the configuration of Fig. 22, is also referred to as a structured detection configuration.
  • the mask 40a is a spatial filter having a light-transmitting region consisting of an opening and a light-non-transmitting region where light is blocked.
  • Illumination light LE from the light source 3 illuminates cells C flowing through the flow path 20 via the illumination optical system 9a.
  • the illumination optical system 9a is a mechanism for uniformly illuminating the flow path 20, and is configured to include a lens. It may also include slits, mirrors, or other optical elements that shape the light.
  • the spatial light modulation unit 4a includes a first lens 41a and a mask 40a.
  • the first lens 41a and the mask 40a are arranged in this order from the side closer to the flow channel 20, and the first lens 41a collects the signal light LSa from the cell C and forms an image on the mask 40a.
  • the first lens 41a is arranged so that the lens surface is parallel to the bottom or top surface of the flow channel 20. The lens surface is in a direction perpendicular to the optical axis of the first lens 41a.
  • the signal light LSa from the cell C is structured by passing through multiple light-transmitting areas of the mask 40a and converted into structured signal light SLSa.
  • the light-transmitting areas on the mask 40a and the position on the flow path 20 where the cell C is illuminated by the illumination light LE are in conjugate positions with respect to the first lens 41a.
  • the signal light LSa from the cell C is given a bright spot pattern (modulation pattern) by the mask 40a, which is installed in the optical path between the flow path 20 and the photodetector 6, and becomes structured signal light SLSa.
  • the structured signal light SLSa structured by the mask 40a is imaged by the light detection optical system 5 and detected by the photodetector 6.
  • structured detection the signal light LSa from the cell C passing through a position on the flow path 20 that is conjugate with the light transmission region of the mask 40a via the first lens 41a passes through the multiple light transmission regions of the mask 40a and is detected in time series by the photodetector 6 as structured signal light SLSa via the light detection optical system 5.
  • the mask 40a is an optical element that imparts a bright spot pattern (modulation pattern) to modulated light from the measurement object irradiated with illumination light when the GMI method is applied in a structured detection configuration.
  • the optical characteristics of the modulated light from the measurement object irradiated with the illumination light are modulated for each of the multiple regions of the mask 40a, and a bright spot pattern (modulation pattern) is given according to the distribution of the multiple regions.
  • the bright spot pattern does not change with time. In other words, the optical characteristics of the multiple regions of the mask 40a do not change with time during measurement by the flow cytometer 1a.
  • the bright spot pattern includes at least two partial patterns that are arranged at different positions in the direction in which the measurement object moves. Furthermore, at least one of the partial patterns is a pattern based on a Fourier basis.
  • the bright spot pattern (modulation pattern) imparted by the mask 40a is generated by a method similar to the bright spot pattern generation method (FIG. 6) described in the first embodiment.
  • the bright spot pattern (modulation pattern) imparted by the mask 40a can also be optimized to improve classification accuracy by the combinatorial optimization method described in the first embodiment.
  • the optical element according to this embodiment (in this embodiment, mask 40a) can impart a highly versatile bright spot pattern (in this embodiment, modulation pattern) that achieves high classification accuracy regardless of the size and shape of the measurement object to modulated light (in this embodiment, signal light LSa) modulated by the measurement object (in this embodiment, cell C), thereby improving the classification accuracy of the flow cytometer 1a for measurement objects of various sizes and shapes.
  • the flow cytometer 1a includes an optical element (in this embodiment, the mask 40a), a light source 3, a microfluidic device 2, a photodetector 6, and an information generating device 10 (PC 8).
  • the light source 3 emits illumination light LE toward a measurement object (in this embodiment, a cell C).
  • the microfluidic device 2 includes a flow channel 20 through which the measurement object (cells C in this embodiment) can flow together with a fluid.
  • the photodetector 6 detects, in a time series manner, the optical signal intensity, which is the intensity of modulated light (in this embodiment, the signal light LSa) emitted from the measurement object (in this embodiment, the cell C).
  • the information generating device 10 (PC 8 ) generates optical information including morphological information of the measurement object (cell C in this embodiment) based on the optical signal intensity detected by the photodetector 6 .
  • the optical element (in this embodiment, the mask 40 a ) is placed midway along the optical path from the measurement object (in this embodiment, the cell C) to the photodetector 6 .
  • the modulated light (in this embodiment, signal light LSa) modulated by the object to be measured (in this embodiment, cell C) is given a bright spot pattern (in this embodiment, modulation pattern) (in this embodiment, structured signal light SLSa) by an optical element (in this embodiment, mask 40a).
  • the flow cytometer 1a can impart a highly versatile bright spot pattern (in this embodiment, a modulation pattern) that achieves high classification accuracy regardless of the size and shape of the object to be measured to the modulated light (in this embodiment, the signal light LSa) modulated by the object to be measured (in this embodiment, a cell C), thereby improving classification accuracy for objects of various sizes and shapes.
  • a modulation pattern in this embodiment, a modulation pattern
  • the signal light LSa modulated by the object to be measured
  • a cell C thereby improving classification accuracy for objects of various sizes and shapes.
  • the signal intensity acquisition unit 110, the optical information generating unit 111, the bright spot pattern generating unit 112, and the combination optimization unit 113 may be realized by a computer.
  • a program for realizing this control function may be recorded in a computer-readable recording medium, and the program recorded in the recording medium may be read into a computer system and executed to realize the control function.
  • the "computer system” here refers to a computer system built into the information generating device 10, and includes hardware such as an operating system (OS) and peripheral devices.
  • OS operating system
  • the "computer-readable recording medium” refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM (Read Only Memory), a CD-ROM (Compact Disc-Read Only Memory), and a storage device such as a hard disk built into the computer system.
  • the term “computer-readable recording medium” may include a medium that dynamically stores a program for a short period of time, such as a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line, and a medium that stores a program for a certain period of time, such as a volatile memory inside a computer system that serves as a server or client in such a case.
  • the above program may be one that realizes part of the above-mentioned functions, or one that can realize the above-mentioned functions in combination with a program already recorded in the computer system.
  • a part or all of the information generating device 10 in the above-mentioned embodiment may be realized as an integrated circuit such as an LSI (Large Scale Integration).
  • LSI Large Scale Integration
  • Each functional block of the information generating device 10 may be individually processed, or may be integrated into a processor in part or in whole.
  • the integrated circuit method is not limited to LSI, and may be realized by a dedicated circuit or a general-purpose processor.
  • an integrated circuit based on that technology may be used.

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Abstract

Un élément optique selon la présente invention communique un motif de point lumineux dans le contexte d'un procédé de mesure qui consiste à émettre une lumière d'éclairage sur une cible de mesure en mouvement et à détecter une lumière modulée à partir de la cible de mesure sur laquelle la lumière d'éclairage a été émise, un motif de point lumineux étant communiqué à la lumière d'éclairage ou à la lumière modulée pour détecter des informations morphologiques concernant la cible de mesure en tant qu'informations optiques. L'élément optique comporte une pluralité de régions qui ont des caractéristiques optiques différentes. Les caractéristiques optiques sont réparties sur la pluralité de régions conformément au motif de point lumineux. Le motif de point lumineux comprend au moins deux motifs partiels qui sont agencés à différentes positions dans la direction de déplacement de la cible de mesure. Au moins un des motifs partiels est fondé sur une base de Fourier.
PCT/JP2024/006080 2023-02-20 2024-02-20 Élément optique, cytomètre en flux, dispositif d'optimisation de motif de point lumineux, procédé d'optimisation de motif de point lumineux et programme WO2024177073A1 (fr)

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021530715A (ja) * 2018-06-13 2021-11-11 シンクサイト株式会社 サイトメトリーのための方法及びシステム

Patent Citations (1)

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Publication number Priority date Publication date Assignee Title
JP2021530715A (ja) * 2018-06-13 2021-11-11 シンクサイト株式会社 サイトメトリーのための方法及びシステム

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ZHANG ZIBANG, LIU SHIJIE, PENG JUNZHENG, YAO MANHONG, ZHENG GUOAN, ZHONG JINGANG: "Simultaneous spatial, spectral, and 3D compressive imaging via efficient Fourier single-pixel measurements", OPTICA, THE OPTICAL SOCIETY, US, vol. 5, no. 3, 20 March 2018 (2018-03-20), US , pages 315, XP093202639, ISSN: 2334-2536, DOI: 10.1364/OPTICA.5.000315 *

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