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US 9,870,614 B1Granted
Automatic 3D brain tumor segmentation and classification
AssigneeSony Corporation
InventorsChen-Rui Chou, Bi Song, Ming-Chang Liu
Life of this patent
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Abstract
A fully automatic brain tumor segmentation and classification method and system improve the healthcare experience with machine intelligence. The automatic brain tumor segmentation and classification method and system utilize whole tumor segmentation and multi-class tumor segmentation to provide accurate analysis.
Claims29
A method programmed in a non-transitory memory of a device comprising:
- performing whole tumor segmentation, wherein the whole tumor segmentation includes:
- utilizing multi-modal MRIs including T1, T1c, Flair and T2;
- determining a least-tumor hemisphere;
- classifying tissue;
- identifying intensity control points based on intensities of different tissue structures;
- normalizing the intensity for each of the MRIs based on the intensity control points;
- locating initial tumor seeds;
- and
- segmenting a tumor based on the tumor seeds;
- and
- performing multi-class tumor segmentation.
8 more elements
An apparatus comprising:
- a non-transitory memory for storing an application, the application for:
- performing whole tumor segmentation, wherein the whole tumor segmentation includes:
- utilizing multi-modal MRIs including T1, T1c, Flair and T2;
- determining a least-tumor hemisphere;
- classifying tissue;
- identifying intensity control points based on intensities of different tissue structures;
- normalizing the intensity for each of the MRIs based on the intensity control points;
- locating initial tumor seeds;
- and
- segmenting a tumor based on the tumor seeds;
- and
- performing multi-class tumor segmentation;
- and
- a processing component coupled to the memory, the processing component configured for processing the application.
11 more elements
A system comprising:
- a magnetic resonance imaging device;
- and
- a computing device configured for:
- performing whole tumor segmentation, wherein the whole tumor segmentation includes:
- utilizing multi-modal MRIs including T1, T1c, Flair and T2;
- determining a least-tumor hemisphere;
- classifying tissue;
- identifying intensity control points based on intensities of different tissue structures:
- normalizing the intensity for each of the MRIs based on the intensity control points;
- locating initial tumor seeds;
- and
- segmenting a tumor based on the tumor seeds;
- and
- performing multi-class tumor segmentation.
11 more elements
A method programmed in a non-transitory memory of a device comprising:
- performing whole tumor segmentation;
- and
- performing multi-class tumor segmentation, wherein the multi-class tumor segmentation includes:
- feature extraction, voxel classification and refinement, wherein feature extraction includes:
- determining voxel-wise features and context features, wherein voxel-wise features include appearance features, texture features and location features, wherein context feature extraction further includes:
- extracting a mean intensity in each octant of an MRI, extracting multiscale context features and combining the context features from a T1c MRI and a T2 MRI.
3 more elements
An apparatus comprising:
- a non-transitory memory for storing an application, the application for:
- performing whole tumor segmentation;
- and
- performing multi-class tumor segmentation, wherein the multi-class tumor segmentation includes:
- feature extraction, voxel classification and refinement, wherein feature extraction includes:
- determining voxel-wise features and context features, wherein voxel-wise features include appearance features, texture features and location features, wherein context feature extraction further includes:
- extracting a mean intensity in each octant of an MRI, extracting multiscale context features and combining the context features from a T1c MRI and a T2 MRI;
- and
- a processing component coupled to the memory, the processing component configured for processing the application.
6 more elements
Dependencies are inferred from the claim text.