ViPSAM: Visual Prompting Medical Image Segmentation Using Segment Anything Model
In proton therapy planning, respiratory-gated non-contrast CT (NCCT) is commonly used for lesion segmentation; however, accurate delineation remains challenging due to low lesion-to-background contrast. Although learning-based methods have shown strong performance, they often struggle with non-contrast image segmentation. Inspired by clinical practice, where contrast-enhanced MRI is referenced to delineate lesions on NCCT, we propose ViPSAM, a visual prompting framework that leverages complementary cross-modality information. Built upon the Segment Anything Model (SAM), ViPSAM introduces a visual prompt encoder to extract guidance features from contrast-enhanced images and a visual-guided...
| Authors | San Lee, Nalee Kim, Jeong Il Yu et al. |
|---|---|
| Primary category | eess.IV |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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