DART: Scaling a Shape Representation Across Anatomical Classes and Imaging Modalities
Current state-of-the-art segmentation models still produce anatomically implausible shapes, such as broken vessels and fragmented glands, on the structures that are hardest to segment. Learned shape representations capture what structures look like and can help correct these errors, but most cover a fixed set of anatomical classes. Extending this coverage across classes and imaging modalities is costly because it often requires new voxel-level annotations. We propose Diffusion for Anatomical Reshaping with Text (DART), which learns a shared shape representation from segmentation probabilities. It uses anatomical descriptions to correct these probabilities without receiving medical images....
| Authors | Koushik Swarna, Pedro R. A. S. Bassi, Lam Ngo et al. |
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| Primary category | cs.CV |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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