US 12,190,523 B2
Acute intracranial hemorrhage segmentation on brain images for volume quantification and individual detection
Youngjin Yoo, Princeton, NJ (US); Eli Gibson, Plainsboro, NJ (US); Bogdan Georgescu, Princeton, NJ (US); Gengyan Zhao, Plainsboro, NJ (US); Thomas Re, New York, NY (US); Jyotipriya Das, Plainsboro, NJ (US); Eva Eibenberger, Nuremberg (DE); and Andrei Chekkoury, Erlangen (DE)
Assigned to Siemens Healthineers AG, Forchheim (DE)
Filed by SIEMENS HEALTHINEERS AG, Forchheim (DE)
Filed on Feb. 15, 2022, as Appl. No. 17/651,107.
Prior Publication US 2023/0316532 A1, Oct. 5, 2023
Int. Cl. G06T 7/12 (2017.01); A61B 6/03 (2006.01); A61B 6/50 (2024.01); G06T 7/00 (2017.01); G06T 7/62 (2017.01)
CPC G06T 7/12 (2017.01) [A61B 6/032 (2013.01); A61B 6/501 (2013.01); G06T 7/0012 (2013.01); G06T 7/62 (2017.01); G06T 2207/10072 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30101 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving an input medical image of a hemorrhage of a patient;
performing a contour-sensitive segmentation of the hemorrhage from the input medical image using a machine learning based contour-sensitive segmentation network;
performing a detection-sensitive segmentation of the hemorrhage from the input medical image using a machine learning based detection-sensitive segmentation network;
determining a final segmentation of the hemorrhage from the input medical image based on results of the contour-sensitive segmentation and results of the detection-sensitive segmentation; and
outputting the final segmentation of the hemorrhage.