# US 2018/0025255 A1 · Classification method and apparatus > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0025255 A1 - Document ID: US20180025255A1 - Status: Published application - Kind: A1 - Application number: 15/216,006 - Filed: 2016-07-21 - Published: 2018-01-25 - Earliest claimed: 2016-07-21 - Assignees: Toshiba Medical Systems Corporation - Applicants: Toshiba Medical Systems Corporation - Inventors: Ian POOLE; Aneta Lisowska; Erin Beveridge; Ruixuan Wang - Web page: https://patentgrep.com/patent/US20180025255A1 ## CPC - G06V 10/7788: {the supervisor being a human, e.g. interactive learning with a human teacher} - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06F 18/2415: based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate - G06F 18/41: {Interactive pattern learning with a human teacher} - G06T 7/0014: {using an image reference approach} - G06T 7/33: using feature-based methods - G06T 7/337: {involving reference images or patches} - G06T 7/68: of symmetry - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/20048: Transform domain processing - G06T 2207/20076: Probabilistic image processing - G06T 2207/20081: Training; Learning - G06T 2207/30016: Brain - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G06V 2201/031: of internal organs - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A medical image data processing apparatus comprises processing circuitry configured to: receive a plurality of sets of medical imaging data; and train a classifier for use in classification, wherein the training of the classifier comprises, for each of the plurality of sets of medical imaging data: selecting a first part and a second part of the respective set of medical imaging data, wherein the first part and the second part are representative of different regions of the same subject; and training the classifier for use in classification based on the first part of the set of medical imaging data and the second part of the set of medical imaging data. ## Claims ### Claim 1 A medical image data processing apparatus comprising processing circuitry configured to: receive a plurality of sets of medical imaging data; and train a classifier for use in classification, wherein the training of the classifier comprises, for each of the plurality of sets of medical imaging data: selecting a first part and a second part of the respective set of medical imaging data, wherein the first part and the second part are representative of different regions of the same subject; and training the classifier for use in classification based on the first part of the set of medical imaging data and the second part of the set of medical imaging data. ### Claim 2 (depends on 1) An apparatus according to claim 1, wherein the first part and the second part of the set of medical imaging data are representative of at least one of: substantially symmetrical regions; or regions having substantially the same shape. ### Claim 3 (depends on 1) An apparatus according to claim 1, wherein: the training of the classifier is for use in determining the presence or absence of at least one abnormality; the first part of the set of medical imaging data is representative of a region on one side of a line of symmetry of anatomical structure represented by the medical imaging data; the second part of the set of medical imaging data is representative of a region on an opposite side of the line of symmetry; and the training of the classifier for use in classification based on the first part of the set of medical imaging data and the second part of the set of medical imaging data comprises training the classifier for use in determining the presence or absence of at least one abnormality in the first part of the set of medical imaging data based at least partially on the second part of the set of medical imaging data and ground truth data associated with the first part of the set of medical imaging data; and wherein the first part of the set of medical imaging data and second part of the set of medical imaging data are representative of corresponding anatomical features on opposite sides of the line of symmetry. ### Claim 4 (depends on 3) An apparatus according to claim 3, wherein the training of the classifier for use in determining the presence or absence of at least one abnormality in the first part of the medical imaging data based at least partially on the second part of the set of medical imaging data is such as to at least partially normalise data values of the first part of the set of medical imaging data relative to the second part of the set of medical imaging data. ### Claim 5 (depends on 3) An apparatus according to claim 3, wherein the processing circuitry is configured to, for each of the sets of medical imaging data, perform a folding process about said line of symmetry in respect of one of the first and second parts of the medical imaging data set such that imaging data of the first part at least partially overlays imaging data of the second part. ### Claim 6 (depends on 1) An apparatus according to claim 1, wherein the processing circuitry is configured to select boundaries of the first and second regions based on at least one of ground truth data, expected position of one or more abnormalities, or a position of at least one selected anatomical feature. ### Claim 7 (depends on 3) An apparatus according to claim 3, wherein the processing circuitry is configured to modify the ground truth data for at least some positions adjacent to regions of abnormality indicated by the ground truth data, and to use the modified ground truth data in training the classifier. ### Claim 8 (depends on 7) An apparatus according to claim 7, wherein the processing circuitry is configured to adjust the importance of at least some of the modified ground truth data in training the classifier. ### Claim 9 (depends on 1) An apparatus according to claim 1, wherein the processing circuitry is further configured to at least one of: use atlas data in training the classifier; use expected or actual position of at least one anatomical feature in training the classifier; align at least one of the medical imaging data sets with at least one atlas data set to obtain registration data, and use the registration data in training the classifier. ### Claim 10 A medical image data processing method comprising: receiving a plurality of sets of medical imaging data; and training a classifier for use in classification, wherein the training of the classifier comprises, for each of the plurality of sets of medical imaging data: selecting a first part and a second part of the respective set of medical imaging data, wherein the first part and the second part are representative of different regions of the same subject; and training the classifier for use in classification based on the first part of the set of medical imaging data and the second part of the set of medical imaging data. ### Claim 11 (depends on 10) A method according to claim 10, wherein the first part and the second part of the set of medical imaging data are at least one of: representative of substantially symmetrical regions; or regions having substantially the same shape. ### Claim 12 (depends on 10) A method according to claim 10, wherein: the training of the classifier is for use in determining the presence or absence of at least one abnormality; the first part of the set of medical imaging data is representative of a region on one side of a line of symmetry of anatomical structure represented by the medical imaging data; the second part of the set of medical imaging data is representative of a region on an opposite side of the line of symmetry; and the training of the classifier for use in classification based on the first part of the set of medical imaging data and the second part of the set of medical imaging data comprises training the classifier for use in determining the presence or absence of at least one abnormality in the first part of the set of medical imaging data based at least partially on the second part of the set of medical imaging data and ground truth data associated with the first part of the set of medical imaging data; and wherein the first part of the set of medical imaging data and second part of the set of medical imaging data are representative of corresponding anatomical features on opposite sides of the line of symmetry. ### Claim 13 (depends on 12) A method according to claim 12, wherein the training of the classifier for use in determining the presence or absence of at least one abnormality in the first part of the medical imaging data based at least partially on the second part of the set of medical imaging data is such as to at least partially normalise data values of the first part of the set of medical imaging data relative to the second part of the set of medical imaging data. ### Claim 14 (depends on 12) A method according to claim 12, further comprising, for each of the sets of medical imaging data, performing a folding process about said line of symmetry in respect of one of the first and second parts of the medical imaging data set such that imaging data of the first part at least partially overlays imaging data of the second part. ### Claim 15 (depends on 10) A method according to claim 10, further comprising selecting boundaries of the first and second regions based on at least one of ground truth data, expected position of one or more abnormalities, or a position of at least one selected anatomical feature. ### Claim 16 (depends on 12) A method according to claim 12, further comprising modifying the ground truth data for at least some positions adjacent to regions of abnormality indicated by the ground truth data, and using the modified ground truth data in training the classifier. ### Claim 17 (depends on 16) A method according to claim 16, further comprising adjusting the importance of at least some of the modified ground truth data in training the classifier. ### Claim 18 (depends on 10) A method according to claim 10, further comprising at least one of: using atlas data in training the classifier; using expected or actual position of at least one anatomical feature in training the classifier; aligning at least one of the medical imaging data sets with at least one atlas data set to obtain registration data, and using the registration data in training the classifier.