mlsanityData to 7 Oct 2026

Biomedical SAM 2: Segment Anything in Biomedical Images and Videos

arXiv 2408.03286 ↗First posted 6 Aug 2024cs.CVcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 33 of its 45 references that are in the map. First posted on arXiv 6 Aug 2024.

Builds on

33 of 45 references.

References · 33

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  5. 2023Segment Anythingcited by 8,158
  6. 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
  7. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  8. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  9. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  10. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  11. 2019Video Object Segmentation using Space-Time Memory Networkscited by 436
  12. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 412
21 more
  1. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 347
  2. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 324
  3. 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291
  4. 2023Customized Segment Anything Model for Medical Image Segmentationcited by 277
  5. 2021Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentationcited by 200
  6. 20192017 Robotic Instrument Segmentation Challengecited by 166
  7. 2020Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinementcited by 101
  8. 2023MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentationcited by 82
  9. 2023SAM on Medical Images: A Comprehensive Study on Three Prompt Modescited by 81
  10. 2018Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Videocited by 78
  11. 2020ISINet: An Instance-Based Approach for Surgical Instrument Segmentationcited by 67
  12. 2020Efficient Semantic Video Segmentation with Per-frame Inferencecited by 63
  13. 2023Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challengecited by 50
  14. 2022Per-Clip Video Object Segmentationcited by 40
  15. 2023The Multi-modality Cell Segmentation Challenge: Towards Universal Solutionscited by 39
  16. 2023SAMAug: Point Prompt Augmentation for Segment Anything Modelcited by 38
  17. 2020Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Videocited by 35
  18. 2023Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentationcited by 35
  19. 2023Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?cited by 22
  20. 2023Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videoscited by 16
  21. 2020Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentationcited by 14

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Citing papers per year: 2024: 6; 2025: 8.

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  1. 2024On Efficient Variants of Segment Anything Model: A Surveycited by 16
  2. 2024Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Surveycited by 14
  3. 2024Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detectioncited by 8
  4. 2025Memory-Augmented SAM2 for Training-Free Surgical Video Segmentationcited by 8
  5. 2024Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenescited by 7
  6. 2024Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentationcited by 5
  7. 2025UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Modelcited by 4
  8. 2024TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentationcited by 3
  9. 2025SurgiSAM2: Fine-tuning a foundational model for surgical video anatomy segmentation and detectioncited by 2
  10. 2025Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approachescited by 2
  11. 2025SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Promptingcited by 1
  12. 2025AI-Driven MRI-based Brain Tumour Segmentation Benchmarkingcited by 0
2 more
  1. 2025Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imagingcited by 0
  2. 2025Backdoor Attacks on Prompt-Driven Video Segmentation Foundation Modelscited by 0