US 12,437,406 B2
Automated medical image and segmentation quality assessment for machine learning tasks
Yi-Qing Wang, Paris (FR); and Giovanni John Jacques Palma, Velizy Villacoublay (FR)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Feb. 25, 2022, as Appl. No. 17/680,799.
Prior Publication US 2023/0274436 A1, Aug. 31, 2023
Int. Cl. G06T 7/00 (2017.01); G06T 7/10 (2017.01)
CPC G06T 7/0016 (2013.01) [G06T 7/10 (2017.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30056 (2013.01); G06T 2207/30168 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, in a medical image computer system, for identifying inconsistencies between specific volumes of medical images, the method comprising:
receiving, by the medical image computer system, a plurality of volumes of medical images, each volume of medical images, of the plurality of volumes of medical images, comprising a plurality of medical images, of an anatomical structure of a patient, captured by a medical imaging equipment;
generating, by the medical image computer system, a plurality of first representation data structures, each first representation data structure of the plurality of first representation data structures corresponding to a volume of medical images in the plurality of volumes of medical images, wherein each first representation data structure of the plurality of first representation data structures comprises first dimensional measurements of the anatomical structure at various locations;
generating, by the medical image computer system, a reference data structure for the anatomical structure of the patient based on the plurality of first representation data structures, wherein the reference data structure comprises second dimensional measurements derived from the first dimensional measurements;
detecting, by the medical image computer system, a discrepancy between a second representation data structure and the reference data structure based on a comparison of the second representation data structure and the reference data structure; and
outputting, by the medical image computer system, a notification of the discrepancy to a computing system based on the detecting of the discrepancy.