	US 12,217,417 B2
	Learning-based domain transformation for medical images
Sidharth Abrol, Bangalore (IN); Bipul Das, Chennai (IN); Vanika Singhal, Bangalore (IN); Amy Deubig, Dousman, WI (US); Sandeep Dutta, Celebration, FL (US); Daphné Gerbaud, Buc (FR); Bianca Sintini, Issy-les-Moulineaux (FR); Ronny Büchel, Wangen bei Olten (CH); and Philipp Kaufmann, Zurich (CH)
Assigned to GE PRECISION HEALTHCARE LLC, Waukesha, WI (US); and UNIVERSITY OF ZURICH, Zurich (CH)
Filed by GE Precision Healthcare LLC, Milwaukee, WI (US); and University of Zurich, Zurich (CH)
Filed on Sep. 9, 2021, as Appl. No. 17/470,076.
Prior Publication US 2023/0071535 A1, Mar. 9, 2023
Int. Cl. G06T 7/00 (2017.01); G06N 20/00 (2019.01); G06T 5/60 (2024.01); G06T 5/70 (2024.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06V 10/25 (2022.01); G16H 30/40 (2018.01)

CPC G06T 7/0012 (2013.01) [G06N 20/00 (2019.01); G06T 5/70 (2024.01); G06T 7/11 (2017.01); G06V 10/25 (2022.01); G16H 30/40 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01)]

17 Claims

8. A computer-implemented method, comprising:

accessing, by a device operatively coupled to a processor, a medical image, wherein the medical image depicts an anatomical structure according to a first medical scanning domain;

identifying, by the device and via execution of a segmentation model, a region-of-interest in the medical image; and

generating, by the device and via execution of a machine learning model, a predicted image based on the medical image, wherein the predicted image depicts the anatomical structure according to a second medical scanning domain that is different from the first medical scanning domain, and wherein the machine learning model is executed on the region-of-interest and not on a remainder of the medical image.