US 11,810,291 B2
Medical image synthesis of abnormality patterns associated with COVID-19
Siqi Liu, Princeton, NJ (US); Bogdan Georgescu, Princeton, NJ (US); Zhoubing Xu, Plainsboro, NJ (US); Youngjin Yoo, Princeton, NJ (US); Guillaume Chabin, Paris (FR); Shikha Chaganti, Princeton, NJ (US); Sasa Grbic, Plainsboro, NJ (US); Sebastien Piat, Lawrence Township, NJ (US); Brian Teixeira, Lawrence Township, NJ (US); Thomas Re, Monroe, NJ (US); and Dorin Comaniciu, Princeton Junction, NJ (US)
Assigned to Siemens Healthcare GmbH, Erlangen (DE)
Filed by Siemens Healthcare GmbH, Erlangen (DE)
Filed on May 1, 2020, as Appl. No. 16/865,266.
Claims priority of provisional application 63/010,198, filed on Apr. 15, 2020.
Prior Publication US 2021/0327054 A1, Oct. 21, 2021
Int. Cl. G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 17/20 (2006.01)
CPC G06T 7/0012 (2013.01) [G06T 7/11 (2017.01); G06T 17/205 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30061 (2013.01)] 13 Claims
OG exemplary drawing
 
1. A computer implemented method comprising:
receiving an input medical image;
generating a synthesized segmentation mask by:
sampling locations from a spatial probability map of abnormality patterns of a disease,
mapping the sampled locations from the spatial probability map to an image space of the synthesized segmentation mask,
generating individual masks each corresponding to a connected component region and positioned at a respective location of the mapped sampled locations in the image space of the synthesized segmentation mask, and
combining the individual masks to generate the synthesized segmentation mask;
masking the input medical image based on the synthesized segmentation mask, the masked input medical image having an unmasked portion and a masked portion;
generating an initial synthesized medical image using a trained machine learning based generator network, the initial synthesized medical image comprising a synthesized version of the unmasked portion of the masked input medical image and synthesized abnormality patterns of the disease in the masked portion of the masked input medical image;
blending the initial synthesized medical image with the input medical image to generate a blended image; and
fusing the synthesized abnormality patterns extracted from the blended image with the input medical image to generate a final synthesized medical image.