Cells

Follow this topic Ask about this topic

Momentum

5 papers in the last four weeks, down 17% on the four weeks before. 0.1% of all new papers.

Jul 6Week of Sep 21

Latest papers 63

All topics

LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images
Jan 6, 2026Matěj Pekár, Vít Musil, Rudolf Nenutil +2Whole-Slide Images Computational Pathology
Masaryk University, Faculty of Informatics, Botanická 68a, Brno, 602 00, Czech Republic · Masaryk Memorial Cancer Institute, Žlutý kopec 7, Brno, 656 53, Czech Republic · BBMRI-ERIC, Neue Stiftingtalstraße 2/B/6, Graz, 8010, Austria
Cell as Point: One-Stage Framework for Efficient Cell Tracking
Nov 22, 2024Yaxuan Song, Jianan Fan, Heng Huang +2Multi-Object Tracking Cells
School of Computer Science, The University of Sydney, Sydney, 2006, NSW, Australia · Department of Computer Science, University of Maryland College Park, College Park, 20742, Maryland, United States · Microsoft, Redmond, 98052, Washington, United States
Programmable Cellular Automata
Date pendingAhmed Khalifa, Muhammad Umair Nasir, Matthew Siper +2Neural Cellular Automata Procedural
Institute of Digital Games, University of Malta, 20 L. Esperanto, L-Imsida, Malta · Nof1, 665 Broadway, Brooklyn, NY 10012, USA · University of the Witwatersrand, 1 Jan Smuts Ave, Braamfontein, Johannesburg, 2017, South Africa +1

Jan 6, 2026, cs.CVJ/K move · Enter open · S save

LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images

Matěj Pekár, Vít Musil, Rudolf Nenutil, Petr Holub+1

Masaryk University, Faculty of Informatics, Botanická 68a, Brno, 602 00, Czech Republic · Masaryk Memorial Cancer Institute, Žlutý kopec 7, Brno, 656 53, Czech Republic · BBMRI-ERIC, Neue Stiftingtalstraße 2/B/6, Graz, 8010, Austria

Whole-Slide Images Computational Pathology Amodal Segmentation Cells Nuclear Data

Open paper AskPDF arXiv

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.

Momentum

Related topics

Latest papers 63

LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images

Cell as Point: One-Stage Framework for Efficient Cell Tracking

Programmable Cellular Automata