Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey (2408.12889v1)

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.

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

This paper presents a comprehensive examination of the application and adaptation of the Segment Anything Model 2 (SAM2) for biomedical images and videos. The authors provide a detailed survey, addressing the challenges and potential of deploying SAM2 within the domain of medical imaging—a field that presents unique challenges distinct from those encountered with natural images.

Overview

The Segment Anything Model (SAM) revolutionized image segmentation by employing a prompt-driven paradigm. SAM2 extends this approach to video segmentation through a streaming-oriented architecture. The paper explores the intricacies of adapting SAM2 to biomedical data, highlighting its capability to reduce annotation burdens and enable zero-shot segmentation. Despite these promising aspects, variations in performance across diverse datasets and tasks are noted, underlining the necessity for domain-specific adaptation.

Technical Insights

SAM2 introduces several advancements over its predecessor, SAM. It features a transformer-based architecture coupled with a memory component that enhances its capacity for real-time video segmentation. This architecture comprises an Image Encoder, Memory Attention module, Prompt Encoder, Mask Decoder, and Memory Bank, which collectively optimize performance for dynamic video scenes.

In the context of medical images, SAM2's extension of its architecture to handle 3D medical imaging as sequences of 2D slices offers a novel strategy. This paper highlights various studies that explore SAM2's zero-shot capabilities in MRI and CT scans, demonstrating variable success that underscores the need for task-specific adaptation and optimization of prompting strategies.

Biomedical Applications

The studies cited in the paper cover extensive applications across both 2D and 3D medical modalities:

• Medical Images: Research indicates SAM2's potential in efficiently handling 3D medical data by treating them akin to video sequences. Despite its utility in significantly limiting the need for manual annotations, the studies exposed performance gaps in fully unsupervised settings.
• Medical Videos: SAM2 shows enhanced capabilities in video segmentation tasks, such as surgical tool tracking, where it outperforms many existing models. Its temporal modeling capability is particularly emphasized in facilitating effective surgical video analysis.

Adaptation and Future Directions

Efforts to bridge the gap between medical and natural imaging involve fine-tuning and adapting SAM2 to the specific qualities of biomedical data. The surveys propose several innovative approaches, like BioSAM2 and MedSAM-2, which aim to enhance the model's applicability through transfer learning and domain-specific adjustments.

Furthermore, domain-specific tools such as Surgical SAM2 optimize resources through efficient memory management, demonstrating the ongoing refinement towards making SAM2 applicable in real-time clinical environments.

Implications and Conclusions

The paper underscores SAM2’s potential to transform biomedical image and video segmentation, contingent on overcoming the domain gap from natural images. It presents SAM2 as a promising tool, albeit one still requiring significant adaptation for consistent outcomes across medical contexts.

Looking forward, the exploration of SAM2 in conjunction with more nuanced medical datasets and scenarios will be crucial. This includes addressing technical challenges like low-contrast scenarios and dynamic visibility changes typical of surgical environments.

In conclusion, while SAM2 avails significant advancements in automated segmentation, its full potential in clinical applications will rely heavily on targeted adaptations, further refinement in real-world medical settings, and continued exploration in diverse medical imaging contexts.