Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation Conference Paper
| Authors: | Jiang, J.; Veeraraghavan, H. |
| Title: | Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation |
| Conference Title: | 23rd International Conference of Medical Image Computing and Computer Assisted Intervention (MICCAI 2020) |
| Abstract: | Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation. Using CT as the labeled source domain, our approach learns to segment multi-modal (T1-weighted and T2-weighted) MRI having no labeled data. Our approach uses a variational auto-encoder (VAE) to disentangle the image content from style. The VAE constrains the style feature encoding to match a universal prior (Gaussian) that is assumed to span the styles of all the source and target modalities. The extracted image style is converted into a latent style scaling code, which modulates the generator to produce multi-modality images according to the target domain code from the image content features. Finally, we introduce a joint distribution matching discriminator that combines the translated images with task-relevant segmentation probability maps to further constrain and regularize image-to-image (I2I) translations. We performed extensive comparisons to multiple state-of-the-art I2I translation and segmentation methods. Our approach resulted in the lowest average multi-domain image reconstruction error of 1.34 ± 0.04. Our approach produced an average Dice similarity coefficient (DSC) of 0.85 for T1w and 0.90 for T2w MRI for multi-organ segmentation, which was highly comparable to a fully supervised MRI multi-organ segmentation network (DSC of 0.86 for T1w and 0.90 for T2w MRI). © 2020, Springer Nature Switzerland AG. |
| Keywords: | computerized tomography; medical imaging; medical computing; image reconstruction; image segmentation; segmentation methods; probability distributions; abdominal organs; codes (symbols); signal encoding; similarity coefficients; disentagled networks; multi-domain translation; unsupervised multi-modal mri segmentation; image translation; joint distributions; multi modality image; multi-organ segmentations; organ segmentation; probability maps |
| Journal Title | Lecture Notes in Computer Science |
| Volume: | 12262 |
| Conference Dates: | 2020 Oct 4-8 |
| Conference Location: | Lima, Peru |
| ISBN: | 0302-9743 |
| Publisher: | Springer |
| Date Published: | 2020-01-01 |
| Start Page: | 347 |
| End Page: | 358 |
| Language: | English |
| DOI: | 10.1007/978-3-030-59713-9_34 |
| PROVIDER: | scopus |
| PUBMED: | 33364627 |
| PMCID: | PMC7757792 |
| DOI/URL: | |
| Notes: | Conference Paper -- Export Date: 2 November 2020 -- Source: Scopus |
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