Evaluation and improvement of Segment Anything Model for interactive
  histopathology image segmentation
SeungKyu Kim Hyun-Jic Oh Seonghui Min Won-Ki Jeong
With the emergence of the Segment Anything Model (SAM) as a foundational
model for image segmentation, its application has been extensively studied
across various domains, including the medical field. However, its potential in
the context of histopathology data, specifically in region segmentation, has
received relatively limited attention. In this paper, we evaluate SAM's
performance in zero-shot and fine-tuned scenarios on histopathology data, with
a focus on interactive segmentation. Additionally, we compare SAM with other
state-of-the-art interactive models to assess its practical potential and
evaluate its generalization capability with domain adaptability. In the
experimental results, SAM exhibits a weakness in segmentation performance
compared to other models while demonstrating relative strengths in terms of
inference time and generalization capability. To improve SAM's limited local
refinement ability and to enhance prompt stability while preserving its core
strengths, we propose a modification of SAM's decoder. The experimental results
suggest that the proposed modification is effective to make SAM useful for
interactive histology image segmentation. The code is available at
\url{https://github.com/hvcl/SAM_Interactive_Histopathology}