ArticleComputational and structural biotechnology journal2024
Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Image-based analysis of lymphoma cell heterogeneity: Comparative study of CRYO and FFPE techniques.Journal of pathology informatics · 2026Article
- NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- NS-GUSL: Green U-Shaped Learning for Nuclei Segmentation from Histopathology Images.Journal of imaging · 2026Article
- Bridging traditional and deep learning methods in H&E histological image normalization: a comprehensive review and introducing a novel framework for comparative analyses.Journal of advanced research · 2026Review
- Enhancing liver fibrosis measurement: Deep learning and uncertainty analysis across multi-center cohorts.Journal of pathology informatics · 2026Article
- Integrating Foundation Model Features into Graph Neural Network and Fusing Predictions with Standard Fine-Tuned Models for Histology Image Classification.Bioengineering (Basel, Switzerland) · 2025Article
- PathoEye: A deep learning framework for the whole-slide image analysis of skin tissue.Computational and structural biotechnology journal · 2025Article
- State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues.Journal of imaging · 2024Review
- NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.Scientific data · 2024Article
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5 authors.
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Abstract
With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological image analysis tasks, nuclei instance segmentation plays a fundamental role in a wide range of clinical and research applications. While many semi- and fully-automatic computerized methods have been proposed for nuclei instance segmentation, deep learning (DL)-based approaches have been shown to deliver the best performances. However, the performance of such approaches usually degrades when tested on unseen datasets. In this work, we propose a novel method to improve the generalization capability of a DL-based automatic segmentation approach. Besides utilizing one of the state-of-the-art DL-based models as a baseline, our method incorporates non-deterministic train time and deterministic test time stain normalization, and ensembling to boost the segmentation performance. We trained the model with one single training set and evaluated its segmentation performance on seven test datasets. Our results show that the proposed method provides up to 4.9%, 5.4%, and 5.9% better average performance in segmenting nuclei based on Dice score, aggregated Jaccard index, and panoptic quality score, respectively, compared to the baseline segmentation model.
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