Gauriau et al., 2015 - Google Patents
Multi-organ localization with cascaded global-to-local regression and shape priorGauriau et al., 2015
View PDF- Document ID
- 6050017552397873951
- Author
- Gauriau R
- Cuingnet R
- Lesage D
- Bloch I
- Publication year
- Publication venue
- Medical image analysis
External Links
Snippet
We propose a method for fast, accurate and robust localization of several organs in medical images. We generalize the global-to-local cascade of regression random forest to multiple organs. A first regressor encodes the global relationships between organs, learning …
- 230000004807 localization 0 title abstract description 73
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20076—Probabilistic image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K2209/05—Recognition of patterns in medical or anatomical images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/0031—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for topological mapping of a higher dimensional structure on a lower dimensional surface
- G06T3/0037—Reshaping or unfolding a 3D tree structure onto a 2D plane
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gauriau et al. | Multi-organ localization with cascaded global-to-local regression and shape prior | |
Mazurowski et al. | Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI | |
Xia et al. | Deep semantic segmentation of kidney and space-occupying lesion area based on SCNN and ResNet models combined with SIFT-flow algorithm | |
Ghesu et al. | Contrastive self-supervised learning from 100 million medical images with optional supervision | |
Ahmad et al. | A lightweight convolutional neural network model for liver segmentation in medical diagnosis | |
Wang et al. | Machine learning and radiology | |
Liang et al. | Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach | |
Islam et al. | Spatially varying label smoothing: Capturing uncertainty from expert annotations | |
Zhou | Medical image recognition, segmentation and parsing: machine learning and multiple object approaches | |
Saito et al. | Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs | |
Zhou | Automatic segmentation of multiple organs on 3D CT images by using deep learning approaches | |
Kumar et al. | Machine learning in medical imaging | |
Farag et al. | A bottom-up approach for automatic pancreas segmentation in abdominal CT scans | |
Huang et al. | Region-based nasopharyngeal carcinoma lesion segmentation from MRI using clustering-and classification-based methods with learning | |
Liu et al. | A semi-supervised convolutional transfer neural network for 3D pulmonary nodules detection | |
WO2022152866A1 (en) | Devices and process for synthesizing images from a source nature to a target nature | |
Javaid et al. | Multi-organ segmentation of chest CT images in radiation oncology: comparison of standard and dilated UNet | |
Konukoglu et al. | Random forests in medical image computing | |
Feng et al. | Supervoxel based weakly-supervised multi-level 3D CNNs for lung nodule detection and segmentation | |
Kéchichian et al. | Automatic 3D multiorgan segmentation via clustering and graph cut using spatial relations and hierarchically-registered atlases | |
Tummala et al. | Liver tumor segmentation from computed tomography images using multiscale residual dilated encoder‐decoder network | |
Ajai et al. | Clustering based lung lobe segmentation and optimization based lung cancer classification using CT images | |
Fredriksen et al. | Teacher-student approach for lung tumor segmentation from mixed-supervised datasets | |
Criminisi et al. | Anatomy detection and localization in 3D medical images | |
Lahoti et al. | Whole Tumor Segmentation from Brain MR images using Multi-view 2D Convolutional Neural Network |