Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types
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Abstract (arXiv)
The distribution and appearance of nuclei are essential markers for the diagnosis and study of cancer. Despite the importance of nuclear morphology, there is a lack of large scale, accurate, publicly accessible nucleus segmentation data. To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process. We have generated nucleus segmentation results in 5,060 Whole Slide Tissue images from 10 cancer types in The Cancer Genome Atlas. One key component of our work is that we carried out a multi-level quality control process (WSI-level and image patch-level), to evaluate the quality of our segmentation results. The image patch-level quality control used manual segmentation ground truth data from 1,356 sampled image patches. The datasets we publish in this work consist of roughly 5 billion quality controlled nuclei from more than 5,060 TCGA WSIs from 10 different TCGA cancer types and 1,356 manually segmented TCGA image patches from the same 10 cancer types plus additional 4 cancer types. Data is available at https://doi.org/10.7937/tcia.2019.4a4dkp9u
Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Feb 2020.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 5; 2024: 0; 2025: 1; 2026: 5.
17 papers.
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- 2023Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Modelcited by 40
- 2021NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationcited by 34
- 2023NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Imagescited by 29
- 2022Data Models for Dataset Drift Controls in Machine Learning With Optical Imagescited by 5
- 2023Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Predictioncited by 5
- 2022MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentationcited by 4
- 2023Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learningcited by 4
- 2021BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentationcited by 3
- 2025Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentationcited by 3
- 2026NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentationcited by 1
- 2021Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimenscited by 0
- 2022Preparing data for pathological artificial intelligence with clinical-grade performancecited by 0
5 more
- 2023Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classificationcited by 0
- 2026NucEval: A Robust Evaluation Framework for Nuclear Instance Segmentationcited by 0
- 2026Sharing standardized image-derived data in computational pathology using DICOMcited by 0
- 2026A Multimodal Dataset for Survival Prediction in Resected Pancreatic Ductal Adenocarcinomacited by 0
- 2026Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentationcited by 0
- 2026Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation3 Oct 2026
- 2026A Multimodal Dataset for Survival Prediction in Resected Pancreatic Ductal Adenocarcinoma24 Sep 2026
- 2026Sharing standardized image-derived data in computational pathology using DICOM13 Sep 2026
- 2026NucEval: A Robust Evaluation Framework for Nuclear Instance Segmentation4 May 2026
- 2026NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation27 Jan 2026
- 2025Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation14 Nov 2025
- 2023Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model16 Nov 2023
- 2023Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning12 Sep 2023
- 2023Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification24 Aug 2023
- 2023NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images3 Aug 2023
- 2023Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Prediction6 Jan 2023
- 2022Data Models for Dataset Drift Controls in Machine Learning With Optical Images4 Nov 2022
5 more
- 2022MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation29 Jun 2022
- 2022Preparing data for pathological artificial intelligence with clinical-grade performance22 May 2022
- 2021Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens19 Aug 2021
- 2021NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation18 Feb 2021
- 2021BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation17 Feb 2021
- 2023Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Modelcited by 40
- 2021NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationcited by 34
- 2023NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Imagescited by 29
- 2022Data Models for Dataset Drift Controls in Machine Learning With Optical Imagescited by 5
- 2023Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Predictioncited by 5
- 2022MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentationcited by 4
- 2023Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learningcited by 4
- 2021BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentationcited by 3
- 2025Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentationcited by 3
- 2026NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentationcited by 1
- 2021Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimenscited by 0
- 2022Preparing data for pathological artificial intelligence with clinical-grade performancecited by 0
12 most cited of 17 All 17 (JSON)
- 2026Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation3 Oct 2026
- 2026A Multimodal Dataset for Survival Prediction in Resected Pancreatic Ductal Adenocarcinoma24 Sep 2026
- 2026Sharing standardized image-derived data in computational pathology using DICOM13 Sep 2026
- 2026NucEval: A Robust Evaluation Framework for Nuclear Instance Segmentation4 May 2026
- 2026NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation27 Jan 2026
- 2025Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation14 Nov 2025
- 2023Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model16 Nov 2023
- 2023Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning12 Sep 2023
- 2023Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification24 Aug 2023
- 2023NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images3 Aug 2023
- 2023Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Prediction6 Jan 2023
- 2022Data Models for Dataset Drift Controls in Machine Learning With Optical Images4 Nov 2022
12 newest of 17 All 17 (JSON)