US 11,727,086 B2
Multimodality image processing techniques for training image data generation and usage thereof for developing mono-modality image inferencing models
Tao Tan, Eindhoven (NL); Gopal B. Avinash, San Ramon, CA (US); Máté Fejes, Szeged (HU); Ravi Soni, San Ramon, CA (US); Dániel Attila Szabó, Debrecen (HU); Rakesh Mullick, Bengaluru (IN); Vikram Melapudi, Bangalore (IN); Krishna Seetharam Shriram, Bengaluru (IN); Sohan Rashmi Ranjan, Bangalore (IN); Bipul Das, Chennai (IN); Utkarsh Agrawal, Bengaluru (IN); László Ruskó, Budapest (HU); Zita Herczeg, Szeged (HU); and Barbara Darázs, Szeged (HU)
Assigned to GE PRECISION HEALTHCARE LLC, Wauwatosa, WI (US)
Filed by GE Precision Healthcare LLC, Milwaukee, WI (US)
Filed on Nov. 10, 2020, as Appl. No. 17/93,960.
Claims priority of application No. 202041042184 (IN), filed on Sep. 29, 2020.
Prior Publication US 2022/0101048 A1, Mar. 31, 2022
Int. Cl. G06F 18/214 (2023.01); G06T 7/30 (2017.01); G06N 5/04 (2023.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); A61B 5/055 (2006.01); A61B 5/00 (2006.01); G06T 5/50 (2006.01); G06F 18/22 (2023.01); G06F 18/28 (2023.01); G06F 18/21 (2023.01)
CPC G06F 18/214 (2023.01) [A61B 5/055 (2013.01); A61B 5/7267 (2013.01); A61B 6/032 (2013.01); A61B 6/5223 (2013.01); G06F 18/2178 (2023.01); G06F 18/22 (2023.01); G06F 18/28 (2023.01); G06N 5/04 (2013.01); G06T 5/50 (2013.01); G06T 7/30 (2017.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G06T 2200/04 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20212 (2013.01); G06T 2207/30004 (2013.01); G06V 2201/03 (2022.01)] 31 Claims
OG exemplary drawing
 
15. A method comprising:
generating, by a system operatively coupled to a processor, a synthetic two-dimensional (2D) image from a three-dimensional (3D) image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient;
projecting, by the system, the 3D image using different projection parameters to generate different synthetic 2D images respectively corresponding to versions of the 3D image in the second capture modality, the different synthetic 2D images including the synthetic 2D image;
selecting, by the system, the synthetic 2D image from amongst the different synthetic 2D images based on a determination that, relative to other synthetic 2D images of the different synthetic 2D images, the synthetic 2D provides a closest match to a native 2D image captured of the same anatomical region of the same patient using the second capture modality; and
transferring, by the system, ground truth data for the 3D image to the synthetic 2D image to generate an annotated synthetic 2D image with the ground truth data.