mlsanityData to 7 Oct 2026

CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images

arXiv 2101.00442 ↗First posted 2 Jan 2021eess.IV cs.CVcited by 50

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Abstract (arXiv)

Nuclei instance segmentation plays an important role in the analysis of Hematoxylin and Eosin (H&E)-stained images. While supervised deep learning (DL)-based approaches represent the state-of-the-art in automatic nuclei instance segmentation, annotated datasets are required to train these models. There are two main types of tissue processing protocols, namely formalin-fixed paraffin-embedded samples (FFPE) and frozen tissue samples (FS). Although FFPE-derived H&E stained tissue sections are the most widely used samples, H&E staining on frozen sections derived from FS samples is a relevant method in intra-operative surgical sessions as it can be performed fast. Due to differences in the protocols of these two types of samples, the derived images and in particular the nuclei appearance may be different in the acquired whole slide images. Analysis of FS-derived H&E stained images can be more challenging as rapid preparation, staining, and scanning of FS sections may lead to deterioration in image quality. In this paper, we introduce CryoNuSeg, the first fully annotated FS-derived cryosectioned and H&E-stained nuclei instance segmentation dataset. The dataset contains images from 10 human organs that were not exploited in other publicly available datasets, and is provided with three manual mark-ups to allow measuring intra-observer and inter-observer variability. Moreover, we investigate the effects of tissue fixation/embedding protocol (i.e., FS or FFPE) on the automatic nuclei instance segmentation performance of one of the state-of-the-art DL approaches. We also create a baseline segmentation benchmark for the dataset that can be used in future research. A step-by-step guide to generate the dataset as well as the full dataset and other detailed information are made available to fellow researchers at https://github.com/masih4/CryoNuSeg.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). Builds on 7 of its 30 references that are in the map. First posted on arXiv 2 Jan 2021.

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7 of 30 references.

References · 7

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2018Panoptic Segmentationcited by 796
  3. 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 387
  4. 2020PanNuke Dataset Extension, Insights and Baselinescited by 113
  5. 2017Machine learning methods for histopathological image analysiscited by 79
  6. 2018Methods for Segmentation and Classification of Digital Microscopy Tissue Imagescited by 72
  7. 2020The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classificationcited by 14

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Citing papers per year: 2021: 1; 2022: 5; 2023: 10; 2024: 11; 2025: 13; 2026: 10.

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  1. 2023CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentationcited by 215
  2. 2023NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Imagescited by 29
  3. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  4. 2023What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentationcited by 24
  5. 2024UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Imagescited by 22
  6. 2023Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentationcited by 17
  7. 2025UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretationcited by 10
  8. 2021Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networkscited by 9
  9. 2024Diversified and Personalized Multi-rater Medical Image Segmentationcited by 9
  10. 2025Segment Anything for Histopathologycited by 9
  11. 2024DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactionscited by 8
  12. 2023Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentationcited by 7
38 more
  1. 2022Unsupervised Tissue Segmentation via Deep Constrained Gaussian Networkcited by 6
  2. 2023Stable Segment Anything Modelcited by 6
  3. 2022Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Imagescited by 5
  4. 2024Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentationcited by 5
  5. 2024NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentationcited by 5
  6. 2024High-Quality Mask Tuning Matters for Open-Vocabulary Segmentationcited by 5
  7. 2024Histopathological Image Classification with Cell Morphology Aware Deep Neural Networkscited by 4
  8. 2025A Multi-Stage Auto-Context Deep Learning Framework for Tissue and Nuclei Segmentation and Classification in H&E-Stained Histological Images of Advanced Melanomacited by 4
  9. 2025SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAMcited by 4
  10. 2022Image Translation Based Nuclei Segmentation for Immunohistochemistry Imagescited by 3
  11. 2022Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Reviewcited by 3
  12. 2023FedSODA: Federated Cross-assessment and Dynamic Aggregation for Histopathology Segmentationcited by 3
  13. 2024MoPEFT: A Mixture-of-PEFTs for the Segment Anything Modelcited by 3
  14. 2025Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentationcited by 3
  15. 2025COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentationcited by 3
  16. 2023A Laplacian Pyramid Based Generative H&E Stain Augmentation Networkcited by 2
  17. 2024Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Datacited by 2
  18. 2025PathFL: Multi-Alignment Federated Learning for Pathology Image Segmentationcited by 2
  19. 2025SAMPO-Path: Segmentation Intent-Aligned Preference Optimization for Pathology Foundation Model Segmentationcited by 2
  20. 2026Disco: Densely-overlapping Cell Instance Segmentation via Adjacency-aware Collaborative Coloringcited by 2
  21. 2026NuNext: Reframing Nucleus Detection as Next-Point Detectioncited by 2
  22. 2026Revisiting foundation models for cell instance segmentationcited by 2
  23. 2023Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classificationcited by 1
  24. 2025AMA-SAM: Adversarial Multi-Domain Alignment of Segment Anything Model for High-Fidelity Histology Nuclei Segmentationcited by 1
  25. 2025GMR-Conv: An Efficient Rotation and Reflection Equivariant Convolution Kernel Using Gaussian Mixture Ringscited by 1
  26. 2025MorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classificationcited by 1
  27. 2026NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentationcited by 1
  28. 2022LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sectionscited by 0
  29. 2024Nuclei Instance Segmentation of Cryosectioned H&E Stained Histological Images using Triple U-Net Architecturecited by 0
  30. 2024CellPilot: A unified approach to automatic and interactive segmentation in histopathologycited by 0
  31. 2025Cancer cytoplasm segmentation in hyperspectral cell image with data augmentationcited by 0
  32. 2025LG-NuSegHop: A Local-to-Global Self-Supervised Pipeline For Nuclei Instance Segmentationcited by 0
  33. 2026Prompt Group-Aware Training for Robust Text-Guided Nuclei Segmentationcited by 0
  34. 2026NucEval: A Robust Evaluation Framework for Nuclear Instance Segmentationcited by 0
  35. 2026One Click per Cell Type Suffices: Training-free Group Interaction for Cell Instance Segmentationcited by 0
  36. 2026HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopycited by 0
  37. 2026A Multimodal Dataset for Survival Prediction in Resected Pancreatic Ductal Adenocarcinomacited by 0
  38. 2026Preference-Guided Adaptation for Open-Vocabulary Semantic Segmentation via Prompt Disagreementcited by 0