Precise and scalable instance segmentation of cell nuclei is essential for computational pathology, yet gigapixel Whole-Slide Images pose major computational challenges. Existing approaches rely on patch-based processing and costly post-processing for instance separation, sacrificing context and efficiency. We introduce LSP-DETR (Local Star Polygon DEtection TRansformer), a fully end-to-end framework that uses a lightweight transformer with linear complexity to process substantially larger images without additional computational cost. Nuclei are represented as star-convex polygons, and a novel radial distance loss function allows the segmentation of overlapping nuclei to emerge naturally, without requiring explicit overlap annotations or handcrafted post-processing. Evaluations on PanNuke and MoNuSeg show strong generalization across tissues and state-of-the-art efficiency, with LSP-DETR being over five times faster than the next-fastest leading method. Code and models are available at https://github.com/RationAI/lsp-detr.

翻译：细胞核的精确且可扩展的实例分割对于计算病理学至关重要，然而千兆像素级别的全切片图像带来了巨大的计算挑战。现有方法依赖于基于图像块的处理以及用于实例分离的昂贵后处理，牺牲了上下文信息与效率。我们提出了LSP-DETR（局部星形多边形检测Transformer），一个完全端到端的框架，它使用一个具有线性复杂度的轻量级Transformer来处理显著更大的图像，而无需额外的计算成本。细胞核被表示为星凸多边形，一种新颖的径向距离损失函数使得重叠细胞核的分割能够自然地实现，无需显式的重叠标注或手工设计的后处理。在PanNuke和MoNuSeg数据集上的评估表明，该方法在不同组织上具有很强的泛化能力以及最先进的效率，LSP-DETR的速度比次优的领先方法快五倍以上。代码和模型可在 https://github.com/RationAI/lsp-detr 获取。