CN111080594B - Slice marker determination method, computer device, and readable storage medium - Google Patents
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Abstract
The present invention relates to a slice marking determination method, a computer device and a readable storage medium, the method comprising: acquiring a medical image to be analyzed; inputting the medical image into a classification network model, and determining the human body part to which the medical image belongs; and inputting the medical image into a regression network model corresponding to the human body part to obtain a mark corresponding to the human body part in the medical image. According to the method, the computer equipment can determine the human body part to which the medical image belongs through the classification network model, then the medical image is input into the regression network model corresponding to the determined human body part to which the medical image belongs, the human body part in the medical image can be accurately marked aiming at the human body part to which the medical image belongs, and the marking accuracy of the human body part corresponding to the obtained medical image is improved.
Description
Technical Field
The present invention relates to the field of medical images, and in particular to a slice marker determination method, a computer device and a readable storage medium.
Background
With the development of computer-aided diagnosis technology, the computer-aided diagnosis technology can assist in finding focus by combining analysis and calculation of a computer through imaging, medical image processing technology and other possible physiological and biochemical means. Before the existing computer-aided diagnosis technology is used, the human body part covered by the input medical image is judged, and then the corresponding computer-aided diagnosis technology is called according to the human body part covered by the determined medical image to diagnose the medical image, so that a more accurate diagnosis result can be obtained, and the judgment of the human body part covered by the medical image is particularly important.
In the traditional technology, labels are distributed in a piecewise linear manner to the head, neck, chest, lung and abdomen and pelvis of a human body according to key points selected in advance, the labels and medical images are input into a neural network for training, and a trained regression network is obtained, so that the human body part covered by the medical images is determined by using the regression network, and the human body part covered in the medical images is identified.
However, the conventional method for recognizing the human body parts has a problem of low recognition accuracy.
Disclosure of Invention
Based on this, it is necessary to provide a slice mark determining method, a computer device and a readable storage medium, aiming at the problem of low recognition accuracy in the conventional method for recognizing the human body part.
In a first aspect, an embodiment of the present invention provides a slice marker determining method, including:
acquiring a medical image to be analyzed;
inputting the medical image into a classification network model, and determining a human body part to which the medical image belongs;
and inputting the medical image into a regression network model corresponding to the human body part to obtain a mark corresponding to the human body part in the medical image.
In one embodiment, the regression network model is pre-labeled with a label of a corresponding human body part, and before the medical image is input into the regression network model corresponding to the human body part to obtain the label corresponding to the human body part in the medical image, the method further includes:
and determining a regression network model corresponding to the human body part according to the corresponding relation between the human body part to which the medical image belongs and the label of the regression network model.
In one embodiment, the training process of the regression network model includes:
acquiring a first sample medical image;
inputting the first sample medical image into a corresponding preset initial regression network model to obtain a sample mark of a human body part in the first sample medical image;
obtaining a value of a loss function of the initial regression network model according to the sample mark and the mark of the human body part in the first sample medical image in advance;
and training the initial regression network model by using the value of the loss function of the initial regression network model to obtain the regression network model.
In one embodiment, training the initial regression network model by using the value of the loss function of the initial regression network model to obtain the regression model includes:
obtaining the value of the Gaussian kernel weighted loss function of the initial regression network model according to the value of the loss function of the initial regression network model;
and training the initial regression network model by utilizing the value of the Gaussian kernel weighted loss function to obtain the regression model.
In one embodiment, the gaussian kernel weighted loss functions of the regression network model corresponding to different body parts are different.
In one embodiment, the loss function of the initial regression network model includes any one of the following functions: a mean square error function; average absolute value error function.
In one embodiment, before the obtaining the value of the loss function of the initial regression network model according to the sample mark and the mark of the human body part in the first sample medical image in advance, the method further includes:
dividing the human body part according to a preset dividing rule to obtain the mark of the human body part in the first sample medical image in advance.
In one embodiment, the training process of the classification network model includes:
acquiring a second sample medical image;
inputting the second sample medical image into a preset initial classification network model, and determining the human body part to which the second sample medical image belongs;
obtaining a value of a loss function of the initial classification network model according to the human body part to which the second sample medical image belongs and a marking result of the second sample medical image in advance;
and training the initial classification network model according to the value of the loss function of the initial classification network model to obtain the classification network model.
In a second aspect, an embodiment of the present invention provides a slice marker determining apparatus, the apparatus including:
the first acquisition module is used for acquiring a medical image to be analyzed;
the first determining module is used for inputting the medical image into a classification network model and determining a human body part to which the medical image belongs;
and the identification module is used for inputting the medical image into a regression network model corresponding to the human body part to obtain a mark corresponding to the human body part in the medical image.
In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a medical image to be analyzed;
inputting the medical image into a classification network model, and determining a human body part to which the medical image belongs;
and inputting the medical image into a regression network model corresponding to the human body part to obtain a mark corresponding to the human body part in the medical image.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring a medical image to be analyzed;
inputting the medical image into a classification network model, and determining a human body part to which the medical image belongs;
and inputting the medical image into a regression network model corresponding to the human body part to obtain a mark corresponding to the human body part in the medical image.
In the method, the device, the computer equipment and the readable storage medium for human body parts provided by the embodiment, the computer equipment acquires the medical image, inputs the medical image into the classification network model, determines the human body part to which the medical image belongs, inputs the medical image into the regression network model corresponding to the determined human body part, and obtains the mark corresponding to the human body part in the medical image. According to the method, the computer equipment can determine the human body part to which the medical image belongs through the classification network model, then the medical image is input into the regression network model corresponding to the determined human body part to which the medical image belongs, the human body part in the medical image can be accurately marked aiming at the human body part to which the medical image belongs, and the marking accuracy of the human body part corresponding to the obtained medical image is improved.
Drawings
FIG. 1 is a schematic diagram of an internal structure of a computer device according to one embodiment;
FIG. 2 is a flow chart of a slice marker determination method according to an embodiment;
FIG. 2 (a) is a schematic illustration of human body part segmentation provided in one embodiment;
FIG. 3 is a flow chart of a slice marker determination method according to another embodiment;
FIG. 4 is a flow chart of a slice marker determination method according to another embodiment;
fig. 5 is a schematic structural view of a slice marker determining apparatus according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The slice marking determining method provided by the embodiment of the application can be applied to the computer equipment shown in fig. 1. The computer device comprises a processor, a memory, and a computer program stored in the memory, wherein the processor is connected through a system bus, and when executing the computer program, the processor can execute the steps of the method embodiments described below. Optionally, the computer device may further comprise a network interface, a display screen and an input means. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium, which stores an operating system and a computer program, an internal memory. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. Optionally, the computer device may be a server, may be a personal computer, may also be a personal digital assistant, may also be other terminal devices, such as a tablet computer, a mobile phone, etc., and may also be a cloud or remote server.
In the traditional automatic human body part identification task, labels are generally distributed in a piecewise linear manner according to key points selected in advance on the head, neck, chest, lung and abdomen and pelvis of a human body, then the labels and images are fed into a neural network for training, in the process, the reliability of the labels corresponding to each image slice is inconsistent, the reliability is lower when the labels are far from the key points, otherwise, the reliability is higher when the labels are close to the key points, and therefore the labels corresponding to the human body parts in medical images cannot be accurately marked through the trained neural network. To this end, an embodiment of the present invention provides a slice marking determining method, a computer device, and a storage medium, which aim to solve the above technical problems of the conventional technology.
The following describes the technical scheme of the present invention and how the technical scheme of the present invention solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.
Fig. 2 is a flow chart of a slice marker determining method according to an embodiment. Fig. 2 (a) is a schematic diagram of human body part division according to an embodiment. The embodiment relates to a specific implementation process of inputting a medical image into a classification network model, determining a human body part to which the medical image belongs, inputting the medical image into a regression network model corresponding to the determined human body part, and obtaining a mark corresponding to the human body part in the medical image. As shown in fig. 2, the method may include:
s201, acquiring a medical image to be analyzed.
Wherein the medical image to be analyzed is a two-dimensional slice image or a 2.5-dimensional slice image. Alternatively, the computer device may acquire the medical image to be analyzed from a PACS (Picture Archiving and Communication Systems, image archiving and communication system) server, or may acquire the medical image to be analyzed from the medical imaging device in real time. Alternatively, the medical image may be a cross-sectional image of a human body.
S202, inputting the medical image into a classification network model, and determining the human body part to which the medical image belongs.
Specifically, the computer device inputs the medical image into the classification network model, and determines the human body part to which the medical image belongs. Alternatively, as shown in fig. 2 (a), the human body part to which the medical image belongs may belong to at least one of the following parts: above the head and neck, below the head and neck, the chest and lung, the abdomen and pelvis and the pubic symphysis. Optionally, the computer device may perform normalization processing, clipping processing, etc. on the medical image before inputting the medical image into the classification network model, and input the processed medical image into the classification network model.
S203, inputting the medical image into a regression network model corresponding to the human body part to obtain a mark corresponding to the human body part in the medical image.
Specifically, the computer device determines a regression network model corresponding to the human body part according to the determined human body part to which the medical image belongs, and inputs the medical image into the regression network model corresponding to the human body part to which the medical image belongs, so as to obtain a mark corresponding to the human body part in the medical image. It should be noted that, the above-mentioned classification network model inputs are slice images, when the marks corresponding to the human body parts to which the complete 3D volume data belong are required to be determined, each layer of medical image of the human body can be input into the above-mentioned classification network model, the human body parts to which each layer of medical image belongs are determined, then the regression network model corresponding to the human body parts to which each layer of medical image belongs is determined, each layer of medical image is input into the corresponding regression network model to obtain the marks corresponding to the human body parts in each layer of medical image, and the marks corresponding to the human body parts in each layer of medical image are post-processed to obtain the marks corresponding to the human body parts to which the complete 3D volume data belong.
In this embodiment, the computer device can determine the human body part to which the medical image belongs through the classification network model, and then input the medical image into the regression network model corresponding to the determined human body part to which the medical image belongs, so that the human body part in the medical image can be accurately marked for the human body part to which the medical image belongs, and the accuracy of marking corresponding to the human body part in the obtained medical image is improved.
The regression model is labeled with a label of a corresponding human body part in advance, and the method further includes, as an optional implementation manner, before S203: and determining the regression network model corresponding to the human body part according to the corresponding relation between the human body part to which the medical image belongs and the label of the regression network model.
The regression model is pre-marked with labels of corresponding human body parts, and the labels of the regression network model labels corresponding to the head and neck parts are exemplified as the head and neck, and the labels of the regression network model labels corresponding to other parts of the human body are analogized. Specifically, after determining the human body part to which the medical image belongs, the computer equipment determines a regression network model corresponding to the human body part to which the medical image belongs according to the corresponding relation between the human body part to which the medical image belongs and the label of the regression model, and inputs the medical image into the regression network model corresponding to the human body part to obtain the mark corresponding to the human body part in the medical image.
In this embodiment, the computer device can quickly and accurately determine the regression network model corresponding to the human body part according to the corresponding relationship between the human body part to which the medical image belongs and the label of the regression model, so that the medical image can be input into the regression network model corresponding to the human body part to which the medical image belongs, the label corresponding to the human body part in the medical image can be quickly and accurately obtained, and the accuracy and efficiency of obtaining the label corresponding to the human body part in the medical image are improved.
Fig. 3 is a flowchart of a slice marker determining method according to another embodiment. The embodiment relates to a specific implementation process of training a regression network model by computer equipment. As shown in fig. 3, based on the above embodiment, as an alternative implementation manner, the training process of the regression network model may include:
s301, acquiring a first sample medical image.
Alternatively, the computer device may obtain the first sample medical image from a PACS (Picture Archiving and Communication Systems, image archiving and communication system) server, or may obtain the first sample medical image from the medical imaging device in real time. The acquired first sample medical image is marked with the human body part to which the first sample medical image belongs.
S302, inputting the first sample medical image into a corresponding preset initial regression network model to obtain a sample mark of the human body part in the first sample medical image.
Specifically, the computer equipment inputs the first sample medical image into a preset initial regression network model corresponding to the human body part to which the first sample medical image belongs, and a sample mark of the human body part in the first sample medical image is obtained. When the first sample medical image is a head and neck image, a preset regression network model corresponding to the first sample medical image is a head and neck regression network model, and the computer equipment inputs the first sample medical image into the corresponding initial head and neck regression network model to obtain a sample mark of a human body part in the first sample medical image as a sample head and neck mark.
S303, obtaining the value of the loss function of the initial regression network model according to the sample marks and the marks of the human body parts in the first sample medical image in advance.
Specifically, the computer device obtains the value of the loss function of the initial regression network model according to the obtained sample mark and the mark of the human body part in the first sample medical image in advance. Optionally, the loss function of the initial regression network model includes any one of the following functions: a mean square error function; average absolute value error function. Alternatively, the loss function of the initial regression network model may also include a deformation function of the mean square error function, or a deformation function of the mean absolute error function, e.g., a smoothed mean absolute error function. It should be noted that, the loss function of the initial regression network model may be a loss function suitable for regression, and is not limited to the above description of the loss function of the initial regression network model, and, for example, the Huber loss function may be used as the loss function of the initial regression network model.
S304, training the initial regression network model by using the value of the loss function of the initial regression network model to obtain a regression network model.
Specifically, the computer device trains the initial regression network model by using the value of the loss function of the initial regression network model, and determines the initial regression network model corresponding to the initial regression network model when the value of the loss function of the initial regression network model reaches a stable value as the regression network model. Optionally, the computer device may obtain a value of a gaussian kernel weighted loss function of the initial regression network model according to a value of a loss function of the initial regression network model, and train the initial regression network model by using the value of the gaussian kernel weighted loss function to obtain the regression model, where the gaussian kernel weighted loss functions of the regression network models corresponding to different human body parts are different. Continuing with the description of the divided body parts shown in fig. 2 (a) above the head and neck, above the chest and lung, below the abdomen, pelvis and pubic symphysis: let the label on top of skull be L 1 The label of the center of the C7 cone is L 2 The label of the center of the T12 cone is L 3 Pubic symphysis label L 4 When the human body part to which the first sample medical image belongs is more than the head and neck, the first sample medical imageThe value of the Gaussian kernel weighted loss function like the corresponding initial regression network model isWherein t is E Range 0 ，Range 0 Representing the head and neck above, wherein sigma is a constant for determining the width of the Gaussian kernel weighted loss function, t is a sign of a human body part in a first sample medical image in advance, x is a sample sign of the human body part in the obtained first sample medical image, f loss (x, t) is the value of the loss function of the initial regression network model corresponding to the case that the human body part to which the first sample medical image belongs is above the head and neck, and is used for measuring the degree of inconsistency between the sample mark x of the human body part in the first sample medical image and the mark t of the human body part in the first sample medical image in advance, and so on, when the human body part to which the first sample medical image belongs is any part of the head and neck, chest, lung and abdomen and pelvis, the value of the Gaussian kernel weighting loss function of the initial regression network model corresponding to the first sample medical image is->Wherein t is E Range 1 ，Range 1 Representing any part in head, neck, chest, lung and abdomen and pelvis, wherein sigma is a constant for determining the width of Gaussian kernel weighted loss function, t is the mark of the human body part in the first sample medical image in advance, x is the sample mark of the human body part in the obtained first sample medical image, f loss (x, t) is the value of the loss function of the corresponding initial regression network model when the human body part to which the first sample medical image belongs is the head and neck, chest and lung and abdomen and pelvis; when the human body part to which the first sample medical image belongs is below pubic symphysis, the value of Gaussian kernel weighted loss function of the initial regression network model corresponding to the first sample medical image is +.>Wherein t is E Range 2 ，Range 2 Below the pubic symphysis, in the formula,sigma is a constant for determining the width of the gaussian kernel weighted loss function, t is a sign of a human body part in the first sample medical image in advance, x is a sample sign of the human body part in the obtained first sample medical image, f loss (x, t) is the value of the loss function of the corresponding initial regression network model when the part of the human body to which the first sample medical image belongs is below the pubic symphysis.
In this embodiment, the computer device inputs the first sample medical image into the corresponding preset initial regression network model to obtain a sample mark of the human body part in the first sample medical image, obtains a value of a loss function of the initial regression network model according to the sample mark and the mark of the human body part in the first sample medical image in advance, accurately trains the initial regression network model by using the value of the loss function of the initial regression network model, improves the accuracy of the obtained regression network model, is better, obtains a value of a gaussian kernel weighted loss function of the initial regression network model according to the value of the loss function of the initial regression network model, and more accurately trains the initial regression network model by using the value of the gaussian kernel weighted loss function, thereby improving the accuracy of the obtained regression model.
On the basis of the foregoing embodiment, as an optional implementation manner, before the step S303, the method further includes: dividing the human body part according to a preset dividing rule to obtain marks of the human body part in the first sample medical image in advance.
Specifically, the computer device divides the human body part according to a preset division rule to obtain a mark of the human body part in the first sample medical image in advance. Alternatively, the computer device may follow the preselected keypoints as shown in FIG. 2 (a): the method comprises the steps of dividing a first sample medical image into a part above the head and neck, a part below the head and neck, a chest and lung, a abdomen and pelvis and a pubic symphysis, obtaining marks of human body parts in the first sample medical image in advance, namely obtaining marks above the head and neck, marks of the chest and lung, marks of the abdomen and pelvis and marks below the pubic symphysis.
In this embodiment, the computer device divides the human body parts according to a preset division rule, so that the marks of the human body parts in the first sample medical image can be obtained quickly, and efficiency of obtaining the loss function of the initial regression network model according to the sample marks and the marks of the human body parts in the first sample medical image in advance is improved.
Fig. 4 is a flowchart of a slice marker determining method according to another embodiment. The embodiment relates to a specific implementation process of training a classification network model by computer equipment. As shown in fig. 4, based on the above embodiment, as an alternative implementation manner, the training process of the classification network model may include:
s401, acquiring a second sample medical image.
Alternatively, the computer device may obtain the second sample medical image from a PACS (Picture Archiving and Communication Systems, image archiving and communication system) server, or may obtain the second sample medical image from the medical imaging device in real time.
S402, inputting the second sample medical image into a preset initial classification network model, and determining the human body part to which the second sample medical image belongs.
Specifically, the computer equipment inputs the second sample medical image into a preset initial classification network model, and determines the human body part to which the second sample medical image belongs. Optionally, the part of the human body to which the second sample medical image belongs may be above the head and neck, or the chest and lung, or the abdomen and pelvis, or below the pubic symphysis.
S403, obtaining the value of the loss function of the initial classification network model according to the human body part to which the second sample medical image belongs and the marking result of the second sample medical image in advance.
Specifically, the computer device obtains the value of the loss function of the initial classification network model according to the obtained human body part to which the second sample medical image belongs and the marking result of the second sample medical image in advance. The value of the loss function of the initial classification network model is used for measuring the difference between the obtained human body part to which the second sample medical image belongs and the marking result of the second sample medical image in advance.
S404, training the initial classification network model according to the value of the loss function of the initial classification network model to obtain the classification network model.
Specifically, the computer device trains the initial classification network model according to the value of the loss function of the initial classification network model, and determines the initial classification network model corresponding to the initial classification network model when the value of the loss function of the initial classification network model reaches a stable value as the classification network model.
In this embodiment, the computer device inputs the second sample medical image into the preset initial classification network model, determines the human body part to which the second sample medical image belongs, obtains the value of the loss function of the initial classification network model according to the human body part to which the second sample medical image belongs and the marking result of the second sample medical image in advance, and can train the initial classification network model more accurately according to the value of the loss function of the initial classification network model, thereby improving the accuracy of the obtained classification network model.
It should be understood that, although the steps in the flowcharts of fig. 2-4 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-4 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or steps.
Fig. 5 is a schematic structural view of a slice marker determining apparatus according to an embodiment. As shown in fig. 5, the apparatus may include: a first acquisition module 10, a first determination module 11 and an identification module 12.
Specifically, the first acquiring module 10 is configured to acquire a medical image to be analyzed;
a first determining module 11, configured to input the medical image into a classification network model, and determine a human body part to which the medical image belongs;
the identification module 12 is configured to input the medical image into a regression network model corresponding to the human body part, so as to obtain a mark corresponding to the human body part in the medical image.
The slice marking determining device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
On the basis of the above embodiment, the regression network model is pre-labeled with a label of a corresponding human body part, and optionally, the apparatus further includes: and a second determination module.
Specifically, the second determining module is configured to determine, according to a correspondence between a human body part to which the medical image belongs and a label of the regression network model, a regression network model corresponding to the human body part.
The slice marking determining device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
On the basis of the above embodiment, optionally, the above apparatus further includes: the system comprises a second acquisition module, a third acquisition module, a fourth acquisition module and a first training module.
Specifically, the second acquisition module is used for acquiring the medical image of the first sample;
the third acquisition module is used for inputting the first sample medical image into a corresponding preset initial regression network model to obtain a sample mark of the human body part in the first sample medical image;
the fourth acquisition module is used for obtaining the value of the loss function of the initial regression network model according to the sample marks and the marks of the human body parts in the first sample medical image in advance;
and the first training module is used for training the initial regression network model by utilizing the value of the loss function of the initial regression network model to obtain the regression network model.
Optionally, the loss function of the initial regression network model is any one of the following functions: a mean square error function; average absolute value error function; a smoothed average absolute error function.
The slice marking determining device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
On the basis of the above embodiment, optionally, the first training module includes an acquiring unit and a training unit.
Specifically, the acquiring unit is configured to obtain a value of a gaussian kernel weighted loss function of the initial regression network model according to the value of the loss function of the initial regression network model;
and the training unit is used for training the initial regression network model by utilizing the value of the Gaussian kernel weighted loss function to obtain a regression model.
Wherein, the Gaussian kernel weighted loss functions of the regression network models corresponding to different human body parts are different.
The slice marking determining device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
Optionally, on the basis of the foregoing embodiment, the foregoing apparatus further includes a fifth acquisition module.
Specifically, the fifth obtaining module is configured to divide the human body part according to a preset division rule, so as to obtain a mark of the human body part in the first sample medical image in advance.
The slice marking determining device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
On the basis of the above embodiment, optionally, the sixth obtaining module, the third determining module, the seventh obtaining module and the second training module of the apparatus are provided.
Specifically, the sixth acquisition module is used for acquiring a second sample medical image;
the third determining module is used for inputting the second sample medical image into a preset initial classification network model and determining the human body part to which the second sample medical image belongs;
a seventh obtaining module, configured to obtain a value of a loss function of the initial classification network model according to a human body part to which the second sample medical image belongs and a marking result of the second sample medical image in advance;
and the second training module is used for training the initial classification network model according to the value of the loss function of the initial classification network model to obtain the classification network model.
The slice marking determining device provided in this embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be described herein.
For specific limitations of the slice marker determination device, reference may be made to the above limitations of the slice marker determination method, and no further description is given here. The respective modules in the slice marking determining apparatus described above may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring a medical image to be analyzed;
inputting the medical image into a classification network model, and determining the human body part to which the medical image belongs;
and inputting the medical image into a regression network model corresponding to the human body part to obtain a mark corresponding to the human body part in the medical image.
The computer device provided in the foregoing embodiments has similar implementation principles and technical effects to those of the foregoing method embodiments, and will not be described herein in detail.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a medical image to be analyzed;
inputting the medical image into a classification network model, and determining the human body part to which the medical image belongs;
and inputting the medical image into a regression network model corresponding to the human body part to obtain a mark corresponding to the human body part in the medical image.
The computer readable storage medium provided in the above embodiment has similar principle and technical effects to those of the above method embodiment, and will not be described herein.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (9)
1. A slice marker determination method, the method comprising:
acquiring a medical image to be analyzed;
inputting the medical image into a classification network model, and determining a human body part to which the medical image belongs; the medical image is a slice image of a single slice;
inputting the medical image into a regression network model corresponding to the human body part to obtain a mark corresponding to the human body part in the medical image; different marks corresponding to human body parts in the medical images are different; different regression network models corresponding to the human body parts are respectively obtained through training;
the training process of the regression network model comprises the following steps:
acquiring a first sample medical image;
inputting the first sample medical image into a corresponding preset initial regression network model to obtain a sample mark of a human body part in the first sample medical image;
obtaining a value of a loss function of the initial regression network model according to the sample mark and the mark of the human body part in the first sample medical image in advance;
training the initial regression network model by using the value of the loss function of the initial regression network model to obtain the regression network model;
the regression network model is pre-marked with labels of corresponding human body parts, and before the medical image is input into the regression network model corresponding to the human body parts to obtain the labels corresponding to the human body parts in the medical image, the method further comprises:
and determining a regression network model corresponding to the human body part according to the corresponding relation between the human body part to which the medical image belongs and the label of the regression network model.
2. The method of claim 1, wherein training the initial regression network model using the values of the loss function of the initial regression network model to obtain the regression model comprises:
obtaining the value of the Gaussian kernel weighted loss function of the initial regression network model according to the value of the loss function of the initial regression network model;
and training the initial regression network model by utilizing the value of the Gaussian kernel weighted loss function to obtain the regression model.
3. The method of claim 2, wherein the gaussian kernel weighted loss functions of the regression network model for different body parts are different.
4. A method according to any of claims 1-3, characterized in that the loss function of the initial regression network model comprises any of the following functions: a mean square error function; average absolute value error function.
5. The method of claim 1, wherein prior to deriving the value of the loss function of the initial regression network model from the sample signature and a signature of a human body site in the first sample medical image in advance, the method further comprises:
dividing the human body part according to a preset dividing rule to obtain the mark of the human body part in the first sample medical image in advance.
6. The method of claim 1, wherein the training process of the classification network model comprises:
acquiring a second sample medical image;
inputting the second sample medical image into a preset initial classification network model, and determining the human body part to which the second sample medical image belongs;
obtaining a value of a loss function of the initial classification network model according to the human body part to which the second sample medical image belongs and a marking result of the second sample medical image in advance;
and training the initial classification network model according to the value of the loss function of the initial classification network model to obtain the classification network model.
7. The method of claim 1, wherein the human body part comprises above the head and neck, below the chest and lung, below the abdomen and pelvis, or below the pubic symphysis.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1-7 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-7.
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| CN109753859A (en) * | 2017-11-08 | 2019-05-14 | 佳能株式会社 | The device and method and image processing system of human part are detected in the picture |
| CN109461161A (en) * | 2018-10-22 | 2019-03-12 | 北京连心医疗科技有限公司 | A method of human organ in medical image is split based on neural network |
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