[Submitted on 22 Oct 2026]
Title:Automated Nucleus Segmentation in Histology Images: An Attention U-Net Approach with Comprehensive Preprocessing and Inference Pipelines
View a PDF of the paper titled Automated Nucleus Segmentation in Histology Images: An Attention U-Net Approach with Comprehensive Preprocessing and Inference Pipelines, by Akshat G and 2 other authors
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Abstract:Objectives: In the area of digital pathology, nucleus segmentation is an essential step for quantitative analysis; however, high nuclear density and staining heterogeneity in histology images pose a great challenge.
Methods: This study implements a lightweight Attention U-Net model on the MoNuSeg 2018 dataset to overcome these challenges. The proposed pipeline integrates comprehensive image and mask preprocessing, including stain deconvolution and multi-pass mask smoothing, with a high-fidelity overlapping patch-stitching strategy during inference on high-resolution images.
Results: The model achieved an Intersection over Union (IoU) score of 0.8745 on full-resolution, unseen test images. This result demonstrates the effectiveness of attention mechanisms in enhancing feature localization and validates the stitching methodology for generating seamless and accurate segmentation maps.
Conclusion: Attention U-Net is an effective and competitive method for automated, scalable nucleus delineation in challenging clinical contexts.
Comments: 8 pages, 8 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.31902 [cs.CV]
(or arXiv:2610.31902v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.31902
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Akshat Gururaj [view email] [v1] Thu, 22 Oct 2026 20:57:54 UTC (6,360 KB)
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