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
https://github.com/hvcl/SAM_Interactive_Histopathology

通过评估 Segment Anything Model (SAM) 在组织病理学数据的零样本和微调场景下的性能，并与其他最先进的交互模型进行比较，我们发现 SAM 在分割性能上相对较弱，而在推理时间和泛化能力方面相对较强。为了改进 SAM 的有限的局部细化能力，并提高其核心优势的同时保持其稳定性，我们提出了对 SAM 解码器的修改，实验证明该修改对于交互式组织学图像分割非常有效。