ArticlePatterns (New York, N.Y.)2023
Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 33 citations in OpenAlex.
- CellPrior-net: Prior-guided nuclei detection and classification for H&E whole-slide images.Journal of pathology informatics · 2026Article
- NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
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- Label Noise in Pathological Segmentation Is Overlooked, Leading to Potential Overestimation of Artificial Intelligence.Cancer science · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- PathOrchestra: a comprehensive foundation model for computational pathology with over 100 diverse clinical-grade tasks.NPJ digital medicine · 2025Article
- Automated cell annotation and classification on histopathology for spatial biomarker discovery.Nature communications · 2025Article
- Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization.Scientific reports · 2025Article
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- Pathology Foundation Models.JMA journal · 2025Review
- A novel dataset for nuclei and tissue segmentation in melanoma with baseline nuclei segmentation and tissue segmentation benchmarks.GigaScience · 2025Article
- Machine learning methods for histopathological image analysis: Updates in 2024.Computational and structural biotechnology journal · 2025Review
- A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction.Patterns (New York, N.Y.) · 2024Article
- Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types.Nature communications · 2024Article
- Towards a general-purpose foundation model for computational pathology.Nature medicine · 2024Article
- Meet the authors: Daisuke Komura and Shumpei Ishikawa.Patterns (New York, N.Y.) · 2023Article
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Authors and funding
12 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Numerous cancer histopathology specimens have been collected and digitized over the past few decades. A comprehensive evaluation of the distribution of various cells in tumor tissue sections can provide valuable information for understanding cancer. Deep learning is suitable for achieving these goals; however, the collection of extensive, unbiased training data is hindered, thus limiting the production of accurate segmentation models. This study presents SegPath-the largest annotation dataset (>10 times larger than publicly available annotations)-for the segmentation of hematoxylin and eosin (H&E)-stained sections for eight major cell types in cancer tissue. The SegPath generating pipeline used H&E-stained sections that were destained and subsequently immunofluorescence-stained with carefully selected antibodies. We found that SegPath is comparable with, or outperforms, pathologist annotations. Moreover, annotations by pathologists are biased toward typical morphologies. However, the model trained on SegPath can overcome this limitation. Our results provide foundational datasets for machine-learning research in histopathology.
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