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DART: One Shape Model That Repairs Broken Segmentation Masks
A text-conditioned 3D diffusion model cleans up masks from TotalSegmentator or MedSAM, on CT and MRI, without reading the image.
Anyone who has shipped automatic segmentation in a viewer knows the pattern. The liver and spleen come back clean, and then the adrenal gland is split into three islands, the esophagus has a gap in the middle, and the gallbladder looks chewed. Radiologists notice these masks first, and they are the ones that make a whole product look careless. Koushik Swarna, Pedro R. A. S. Bassi, Lam Ngo, Wenxuan Li, Jiancheng Yang, Alan Yuille and Zongwei Zhou (Johns Hopkins University, Aalto University, the ELLIS Institute Finland and Johns Hopkins Medicine) target those structures with DART, posted as arXiv:2610.27605 on 20 October 2026. DART stands for Diffusion for Anatomical Reshaping with Text. It is one 3D diffusion model that takes the probability map from any segmentation model, plus a short text description of the target structure, and returns a corrected mask. It never sees the CT or MRI.
Why a viewer or platform team should care
Most correction steps we see in production are hand-written rules: keep the largest connected component, fill holes, close small…
