Segment Any Medical Model Extended
Yihao Liu Jiaming Zhang Andres Diaz-Pinto Haowei Li Alejandro Martin-Gomez Amir Kheradmand Mehran Armand
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.