PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation-based MRI segmentation Journal Article
| Authors: | Jiang, J.; Hu, Y. C.; Tyagi, N.; Rimner, A.; Lee, N.; Deasy, J. O.; Berry, S.; Veeraraghavan, H. |
| Article Title: | PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation-based MRI segmentation |
| Abstract: | We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ segmentation from magnetic resonance (MRI) images. Our UDA approach models the co-dependency between images and their segmentation as a joint probability distribution using a new structure discriminator. The structure discriminator computes structure of interest focused adversarial loss by combining the generated pseudo MRI with probabilistic segmentations produced by a simultaneously trained segmentation sub-network. The segmentation sub-network is trained using the pseudo MRI produced by the generator sub-network. This leads to a cyclical optimization of both the generator and segmentation sub-networks that are jointly trained as part of an end-to-end network. Extensive experiments and comparisons against multiple state-of-the-art methods were done on four different MRI sequences totalling 257 scans for generating multi-organ and tumor segmentation. The experiments included, (a) 20 T1-weighted (T1w) in-phase mdixon and (b) 20 T2-weighted (T2w) abdominal MRI for segmenting liver, spleen, left and right kidneys, (c) 162 T2-weighted fat suppressed head and neck MRI (T2wFS) for parotid gland segmentation, and (d) 75 T2w MRI for lung tumor segmentation. Our method achieved an overall average DSC of 0.87 on T1w and 0.90 on T2w for the abdominal organs, 0.82 on T2wFS for the parotid glands, and 0.77 on T2w MRI for lung tumors. © 1982-2012 IEEE. |
| Keywords: | magnetic resonance imaging; lung tumor; tumors; magnetic resonance; biological organs; image segmentation; probability distributions; tumor segmentation; abdominal organs; parotid glands; generative adversarial network; adversarial networks; domain adaptation; multi-organ segmentations; end-to-end network; mri segmentation; unsupervised domain adaptation; image distributions; joint probabilistic; joint probability distributions |
| Journal Title: | IEEE Transactions on Medical Imaging |
| Volume: | 39 |
| Issue: | 12 |
| ISSN: | 0278-0062 |
| Publisher: | IEEE |
| Date Published: | 2020-12-01 |
| Start Page: | 4071 |
| End Page: | 4084 |
| Language: | English |
| DOI: | 10.1109/tmi.2020.3011626 |
| PUBMED: | 32746148 |
| PROVIDER: | scopus |
| PMCID: | PMC7757913 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 4 January 2021 -- Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
Related MSK Work