Segment Any Medical Model Extended
This work addresses the need for improved foundation models in medical image segmentation, but it is incremental as it builds on existing SAM variants without introducing a fundamentally new method.
The authors tackled the limited performance of the Segment Anything Model (SAM) on medical images by introducing SAMM Extended (SAMME), a unified platform that integrates new SAM variants, adopts faster communication protocols, accommodates new interactive modes, and allows fine-tuning of subcomponents, with results applicable to image-guided therapy and other medical applications.
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.