Shape-Consistent Generative Adversarial Networks for Multi-Modal Medical Segmentation Maps

Leo Segre, Or Hirschorn, Dvir Ginzburg, Dan Raviv

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Image translation across domains for unpaired datasets has gained interest and great improvement lately. In medical imaging, there are multiple imaging modalities, with very different characteristics. Our goal is to use cross-modality adaptation between CT and MRI whole cardiac scans for semantic segmentation. We present a segmentation network using synthesised cardiac volumes for extremely limited datasets. Our solution is based on a 3D cross-modality generative adversarial network to share information between modalities and generate synthesized data using unpaired datasets. Our network utilizes semantic segmentation to improve generator shape consistency, thus creating more realistic synthesised volumes to be used when re-training the segmentation network. We show that improved segmentation can be achieved on small datasets when using spatial augmentations to improve a generative adversarial network. These augmentations improve the generator capabilities, thus enhancing the performance of the Segmentor. Using only 16 CT and 16 MRI cardiovascular volumes, improved results are shown over other segmentation methods while using the suggested architecture. Our code is publicly available1.

Original languageEnglish
Title of host publicationISBI 2022 - Proceedings
Subtitle of host publication2022 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
ISBN (Electronic)9781665429238
StatePublished - 2022
Event19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 - Kolkata, India
Duration: 28 Mar 202231 Mar 2022

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference19th IEEE International Symposium on Biomedical Imaging, ISBI 2022


FundersFunder number
Zimin Institute for Engineering Solutions Advancing Better Lives
Tel Aviv University
Nicholas and Elizabeth Slezak Super Center for Cardiac Research and Biomedical Engineering


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