Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey

• URL: http://arxiv.org/abs/2408.12889v1
• Date: Fri, 23 Aug 2024 07:51:10 GMT
• Title: Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey
• Authors: Yichi Zhang, Zhenrong Shen,
• Abstract summary: Segment Anything Model (SAM) signifies a noteworthy expansion of the prompt-driven paradigm into the domain of image segmentation. Recent introduction of SAM2 effectively extends the original SAM to a streaming fashion and demonstrates strong performance in video segmentation. This paper presents an overview of recent efforts in applying and adapting SAM2 to biomedical images and videos.
• Score: 8.216028136706948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The unprecedented developments in segmentation foundational models have become a dominant force in the field of computer vision, introducing a multitude of previously unexplored capabilities in a wide range of natural images and videos. Specifically, the Segment Anything Model (SAM) signifies a noteworthy expansion of the prompt-driven paradigm into the domain of image segmentation. The recent introduction of SAM2 effectively extends the original SAM to a streaming fashion and demonstrates strong performance in video segmentation. However, due to the substantial distinctions between natural and medical images, the effectiveness of these models on biomedical images and videos is still under exploration. This paper presents an overview of recent efforts in applying and adapting SAM2 to biomedical images and videos. The findings indicate that while SAM2 shows promise in reducing annotation burdens and enabling zero-shot segmentation, its performance varies across different datasets and tasks. Addressing the domain gap between natural and medical images through adaptation and fine-tuning is essential to fully unleash SAM2's potential in clinical applications. To support ongoing research endeavors, we maintain an active repository that contains up-to-date SAM & SAM2-related papers and projects at https://github.com/YichiZhang98/SAM4MIS.

Related papers

• DB-SAM: Delving into High Quality Universal Medical Image Segmentation [100.63434169944853]
  We propose a dual-branch adapted SAM framework, named DB-SAM, to bridge the gap between natural and 2D/3D medical data. Our proposed DB-SAM achieves an absolute gain of 8.8%, compared to a recent medical SAM adapter in the literature.
  arXiv  Detail & Related papers  (2024-10-05T14:36:43Z)
• SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation [51.90445260276897]
  We prove that the Segment Anything Model 2 (SAM2) can be a strong encoder for U-shaped segmentation models. We propose a simple but effective framework, termed SAM2-UNet, for versatile image segmentation.
  arXiv  Detail & Related papers  (2024-08-16T17:55:38Z)
• Biomedical SAM 2: Segment Anything in Biomedical Images and Videos [32.818587990862426]
  BioSAM-2 is 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.
  arXiv  Detail & Related papers  (2024-08-06T16:34:04Z)
• Segment Anything for Videos: A Systematic Survey [52.28931543292431]
  The recent wave of foundation models has witnessed tremendous success in computer vision (CV) and beyond. The segment anything model (SAM) has sparked a passion for exploring task-agnostic visual foundation models. This work conducts a systematic review on SAM for videos in the era of foundation models.
  arXiv  Detail & Related papers  (2024-07-31T02:24:53Z)
• Segment Any Medical Model Extended [39.80956010574076]
  We introduce SAMM Extended (SAMME), a platform that integrates new SAM variant models, adopts faster communication protocols, accommodates new interactive modes, and allows for fine-tuning of subcomponents of the models. These features can expand the potential of foundation models like SAM, and the results can be translated to applications such as image-guided therapy, mixed reality interaction, robotic navigation, and data augmentation.
  arXiv  Detail & Related papers  (2024-03-26T21:37:25Z)
• Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions [8.216028136706948]
  The recent introduction of the Segment Anything Model (SAM) signifies a noteworthy expansion of the prompt-driven paradigm into the domain of image segmentation. We provide a comprehensive overview of recent endeavors aimed at extending the efficacy of SAM to medical image segmentation tasks. We explore potential avenues for future research directions in SAM's role within medical image segmentation.
  arXiv  Detail & Related papers  (2024-01-07T14:25:42Z)
• MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation [58.53672866662472]
  We introduce a modality-agnostic SAM adaptation framework, named as MA-SAM. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data.
  arXiv  Detail & Related papers  (2023-09-16T02:41:53Z)
• SAM-Med2D [34.82072231983896]
  We introduce SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images. We first collect and curate approximately 4.6M images and 19.7M masks from public and private datasets. We fine-tune the encoder and decoder of the original SAM to obtain a well-performed SAM-Med2D.
  arXiv  Detail & Related papers  (2023-08-30T17:59:02Z)
• A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering [49.732628643634975]
  The Segment Anything Model (SAM), developed by Meta AI Research, offers a robust framework for image and video segmentation. This survey provides a comprehensive exploration of the SAM family, including SAM and SAM 2, highlighting their advancements in granularity and contextual understanding.
  arXiv  Detail & Related papers  (2023-05-12T07:21:59Z)
• Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey [8.76496233192512]
  We discuss efforts to extend the success of the Segment Anything Model to medical image segmentation tasks. Many insights are drawn to guide future research to develop foundation models for medical image analysis.
  arXiv  Detail & Related papers  (2023-05-05T16:48:45Z)
• Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation [51.770805270588625]
  The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation. Recent studies and individual experiments have shown that SAM underperforms in medical image segmentation. We propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model.
  arXiv  Detail & Related papers  (2023-04-25T07:34:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.