Refining 3D Medical Segmentation with Verbal Instruction
Accurate 3D anatomical segmentation is essential for clinical diagnosis and surgical planning. However, automated models frequently generate suboptimal shape predictions due to factors such as limited and imbalanced training data, inadequate labeling quality, and distribution shifts between training and deployment settings. A natural solution is to iteratively refine the predicted shape based on the radiologists' verbal instructions. However, this is hindered by the scarcity of paired data that explicitly links erroneous shapes to corresponding corrective instructions. As an initial step toward addressing this limitation, we introduce CoWTalk, a benchmark comprising 3D arterial anatomies ...
| Authors | Kangxian Xie, Jiancheng Yang, Nandor Pinter et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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