Nuclear instance segmentation in histology images is crucial for a broad spectrum of clinical applications. Current prevailing nuclear instance segmentation algorithms rely on regression of nuclei contours, distance maps, watershed markers or a proxy nuclear representation of star-convex polygons. Consequently, these methods necessitate sophisticated post-processing operations to distinguish nuclei instances, which are commonly acknowledged to be error-prone and parameter-sensitive. Recently, the segment anything model (SAM) has earned attracted huge attention within the domain of medical image segmentation due to its impressive generalization ability and promptable property. Nevertheless, its potential on nuclear instance segmentation remains largely underexplored. In this paper, we present a novel prompt-driven framework that consists of a point prompter and a SAM for automatic nuclei instance segmentation. Specifically, the prompter learns to generate a unique point prompt for each nucleus while the SAM is fine tuned to output the corresponding mask of the cued nucleus. Furthermore, we propose to add adjacent nuclei as negative prompts to promote the model's ability to recognize overlapping nuclei. Without bells and whistles, our proposed method sets a new state-of-the-art performance on three challenging benchmarks. Our code is available at \textcolor{magenta}{\url{https://github.com/windygoo/PromptNucSeg}} .

本研究提出了一种新颖的基于提示的框架，包括一个点提示器和一个用于自动核实例分割的段装置模型。通过学习为每个细胞核生成唯一的点提示，并将段装置模型微调为输出相应的细胞核掩模，我们的方法在三个具有挑战性的基准测试上取得了最新的性能。

释放基于提示驱动的核实例分割的力量