Abstract
The Segment Anything Model (SAM) has drawn significant attention from researchers who work on medical image segmentation because of its generalizability. However, researchers have found that SAM may have limited performance on medical images compared to state-of-the-art non-foundation models. Regardless, the community sees potential in extending, fine-tuning, modifying, and evaluating SAM for analysis of medical imaging. An increasing number of works have been published focusing on the mentioned four directions, where variants of SAM are proposed. To this end, a unified platform helps push the boundary of the foundation model for medical images, facilitating the use, modification, and validation of SAM and its variants in medical image segmentation. In this work, 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.
Abstract (translated)
segment Anything Model (SAM) 引起了医疗图像分割领域研究人员的广泛关注，因为它具有很好的泛化能力。然而，研究人员发现，与最先进的非基础模型相比，SAM在医疗图像上的表现可能有限。尽管如此，社区认为，扩展、微调、修改和评估SAM在医疗图像分割分析中的应用是具有潜力的。越来越多的研究聚焦于提到的四个方向，提出了各种SAM变体。为此，一个统一平台有助于扩大基础模型（SAM）在医疗图像领域的边界，促进SAM及其变体的使用、修改和验证。在这方面，我们介绍了 SAMME，一个集成了新SAM变体模型的统一平台，采用了更快的通信协议，支持新的交互模式，并允许对模型的子组件进行微调。这些功能可以扩展像SAM这样的基础模型的潜力，并且这些结果可以应用于诸如图像指导治疗、混合现实交互、机器人导航和数据增强等应用中。
URL
https://arxiv.org/abs/2403.18114
