LSP-DETR：在全滑动图像中高效且可扩展的核分割
LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images
AI总结：
LSP-DETR通过轻量级Transformer和星形凸多边形表示，在全滑动图像中实现高效且可扩展的细胞核分割，展现出卓越的效率和泛化能力。
摘要：
精确且可扩展的细胞核实例分割对于计算病理学至关重要，但吉像素全滑动图像带来了重大计算挑战。现有方法依赖于基于补丁的处理和昂贵的后处理进行实例分离，牺牲了上下文和效率。我们引入LSP-DETR（局部星形多边形检测Transformer），一个完全端到端的框架，使用线性复杂度的轻量级变压器来处理远大于常规图像的图像，而无需额外计算成本。细胞核被表示为星形凸多边形，且一种新的径向距离损失函数允许重叠细胞核的分割自然出现，无需要求显式重叠标注或手工后处理。在PanNuke和MoNuSeg上的评估显示，LSP-DETR在不同组织中具有强大的泛化能力，并且具有最先进的效率，LSP-DETR比次快的领先方法快五倍以上。代码和模型可在https://github.com/RationAI/lsp-detr上获得。
英文摘要：
Background and Objective: Precise and scalable instance segmentation of cell nuclei is a fundamental prerequisite for computational pathology, yet gigapixel whole-slide images (WSIs) pose significant computational challenges. While patch-based processing is standard during training, existing methods are often limited to small tile sizes during inference due to architectural bottlenecks or reliance on computationally expensive post-processing for instance separation. We introduce a faster, scalable, and end-to-end framework capable of processing large-scale image tiles while accurately modeling biologically realistic overlapping nuclei. Methods: We propose LSP-DETR (Local Star Polygon DEtection TRansformer). The model represents nuclei as star-convex polygons and employs a lightweight transformer with linear complexity, enabling the processing of high-resolution images in a single forward pass. A novel radial distance loss accommodates annotation uncertainty, allowing the segmentation of overlapping nuclei to emerge naturally without explicit overlap labels. Results: LSP-DETR achieves state-of-the-art efficiency, with an inference time of 0.45 s/mm^2, a 3.2x speedup over StarDist, the next-fastest method. On PanNuke, the model achieves competitive accuracy (67.5 bPQ), while yielding an F$_1$-score of 0.964 in polygon overlap when evaluated against consensus annotations from two expert pathologists. Furthermore, it outperforms larger models such as LKCell in generalization robustness, reaching an F1-score of 85.0 on MoNuSeg. Conclusions: LSP-DETR bridges the gap between high-fidelity segmentation and practical clinical requirements by eliminating heuristic post-processing. By providing a scalable, linear-complexity solution that naturally handles overlaps between nuclei, this framework sets a new direction for efficient high-throughput WSI analysis in digital pathology.
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