Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation
Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities. While foundation models like the Segment Anything Model (SAM) show promise, they often struggle with medical images without specific adaptation. Moreover, point prompts, despite being the most natural form of user interaction, provide insufficient spatial context for reliable segmentation, particularly when target structures are irregular or poorly contrasted. In this paper, we propose an enhanced segmentation framework that integrates a lightweight Box Predictor module into the MedSAM architecture. The Box Predictor estimates an approximate bounding box...
| Authors | Amirhossein Movahedisefat, Amirreza Fateh, Mohammad Reza Mohammadi |
|---|---|
| Primary category | cs.CV |
| Published | 3 June 2026 |
| Last updated at source | 25 July 2026 |
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