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}} .

翻译：摘要：组织学图像中的细胞核实例分割对于广泛的临床应用至关重要。当前主流的细胞核实例分割算法依赖于细胞核轮廓回归、距离图、分水岭标记或星凸多边形的代理细胞核表示。因此，这些方法需要复杂的后处理操作来区分细胞核实例，这一过程通常被认为容易出错且对参数敏感。最近，分段任意模型（SAM）因其令人印象深刻的泛化能力和可提示特性，在医学图像分割领域引起了广泛关注。然而，其在细胞核实例分割中的潜力尚未得到充分探索。本文提出了一种新颖的提示驱动框架，该框架由点提示器和SAM组成，用于自动细胞核实例分割。具体来说，提示器学习为每个细胞核生成唯一的点提示，同时SAM被微调以输出相应提示细胞核的分割掩码。此外，我们提出将相邻细胞核作为负提示，以提升模型识别重叠细胞核的能力。无需额外复杂设计，我们提出的方法在三个具有挑战性的基准测试中取得了最新最优性能。我们的代码可在 \textcolor{magenta}{\url{https://github.com/windygoo/PromptNucSeg}} 获取。