ArticleScientific data2024
NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- SiNuS: A comprehensive dataset for singular nuclei segmentation forData in brief · 2026Article
- Toward comprehensive cellular characterization of H&E slides.Journal of pathology informatics · 2026Article
- NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- SwiftMSeg: lightweight multi-scale local-global context modeling with transformer for medical image segmentation.Scientific reports · 2026Article
- Article
- NucVerse3D: generalizable 3D nuclear instance segmentation across heterogeneous microscopy modalities.Scientific reports · 2026Article
- Development of Human Conjunctival Goblet Cell Segmentation Datasets to Improve Quantitation.Scientific data · 2026Article
- A multimodal explainable ai framework to assist in the differential diagnosis of head and neck reactive follicular hyperplasia and follicular lymphoma: an international multicentre study.Virchows Archiv : an international journal of pathology · 2026Article
- Adaptive compression framework for giga-pixel whole slide images.Nature communications · 2025Article
- CellSAM: a foundation model for cell segmentation.Nature methods · 2025Article
- Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization.Scientific reports · 2025Article
- Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.Communications medicine · 2025Article
- CellBinDB: a large-scale multimodal annotated dataset for cell segmentation with benchmarking of universal models.GigaScience · 2025Article
- A novel dataset for nuclei and tissue segmentation in melanoma with baseline nuclei segmentation and tissue segmentation benchmarks.GigaScience · 2025Article
- Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization.Computational and structural biotechnology journal · 2024Article
- NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mouse-to-Human Domain Transfer.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2024Article
- NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.Scientific data · 2024Article
- A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images.Frontiers in medicine · 2022Article
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9 authors.
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Abstract
In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to classical machine learning and image processing techniques. However, these models need fully annotated datasets for training which is challenging to acquire, especially in the medical domain. In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg. This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs. Moreover, for the first time, we provide additional ambiguous area masks for the entire dataset. These vague areas represent the parts of the images where precise and deterministic manual annotations are impossible, even for human experts. The dataset and detailed step-by-step instructions to generate related segmentation masks are publicly available on the respective repositories.
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