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Biomedical SAM 2: Segment Anything in Biomedical Images and Videos

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arxiv 2408.03286 v2 pith:MIYLJBDO submitted 2024-08-06 cs.CV

Zhiling Yan , Weixiang Sun , Rong Zhou , Zhengqing Yuan , Kai Zhang , Yiwei Li , Tianming Liu , Quanzheng Li
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Xiang Li Lifang He Lichao Sun
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classification cs.CV
keywords segmentationbiomedicalsam-2foundationimagemedicalmodelsanything
verification ladder T0 review T1 audit T2 compute T3 formal
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Medical image segmentation and video object segmentation are essential for diagnosing and analyzing diseases by identifying and measuring biological structures. Recent advances in natural domain have been driven by foundation models like the Segment Anything Model 2 (SAM-2). To explore the performance of SAM-2 in biomedical applications, we designed three evaluation pipelines for single-frame 2D image segmentation, multi-frame 3D image segmentation and multi-frame video segmentation with varied prompt designs, revealing SAM-2's limitations in medical contexts. Consequently, we developed BioSAM-2, an enhanced foundation model optimized for biomedical data based on SAM-2. Our experiments show that BioSAM-2 not only surpasses the performance of existing state-of-the-art foundation models but also matches or even exceeds specialist models, demonstrating its efficacy and potential in the medical domain.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A tuned DeepLabv3 achieves 97.5% IoU on iPS colony segmentation, outpacing SAM2 (81.0%) and MedSAM2 (63.5%) under the authors' test conditions.

  2. Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    MA-SAM2 adds context-aware and occlusion-resilient memory to SAM2 and reports Challenge IoU of 62.49 percent on EndoVis2017 and 64.40 percent on EndoVis2018, beating SAM2 by 6.10 and 4.36 points.

  3. AI-Driven MRI-based Brain Tumour Segmentation Benchmarking

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Fine-tuned SAM and SAM 2 with high-quality bounding-box prompts achieve higher Dice scores than zero-shot nnU-Net on pediatric brain tumor segmentation, but nnU-Net remains more practical.

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