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}

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。

评估和改进交互式组织病理学图像分割的“任意分割模型