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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.03163 |
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| _version_ | 1866911357013590016 |
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| author | Pekár, Matěj Musil, Vít Nenutil, Rudolf Holub, Petr Brázdil, Tomáš |
| author_facet | Pekár, Matěj Musil, Vít Nenutil, Rudolf Holub, Petr Brázdil, Tomáš |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03163 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images Pekár, Matěj Musil, Vít Nenutil, Rudolf Holub, Petr Brázdil, Tomáš Computer Vision and Pattern Recognition 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. |
| title | LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.03163 |