SAM 3 outperforms SAM 2 under click prompting for zero-shot 3D medical segmentation across 16 datasets and 54 structures, with fewer failure modes in prompt-frame over-segmentation and prediction retention.
arXiv preprint arXiv:2408.00756 (2024)
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Enhances MedSAM with a 1.6M-parameter Box Predictor trained in two stages to convert single clicks to bounding boxes, reporting Dice scores of 0.89-0.98 on four medical datasets across CT, MRI, and ultrasound.
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Comparing SAM 2 and SAM 3 for Zero-Shot Segmentation of 3D Medical Data
SAM 3 outperforms SAM 2 under click prompting for zero-shot 3D medical segmentation across 16 datasets and 54 structures, with fewer failure modes in prompt-frame over-segmentation and prediction retention.
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Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation
Enhances MedSAM with a 1.6M-parameter Box Predictor trained in two stages to convert single clicks to bounding boxes, reporting Dice scores of 0.89-0.98 on four medical datasets across CT, MRI, and ultrasound.
- On Efficient Variants of Segment Anything Model: A Survey