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

arXiv: https://arxiv.org/abs/2101.00442 · HTML: https://mlsanity.com/paper/2101.00442/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 2 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- [Methods for Segmentation and Classification of Digital Microscopy Tissue Images](https://mlsanity.com/paper/1810.13230/) (2018)
- [The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification](https://mlsanity.com/paper/2008.12602/) (2020)

## Built on by (most cited first, 30 of 51)

- [CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2303.11797/) (2023)
- [NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images](https://mlsanity.com/paper/2308.01760/) (2023)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2306.15521/) (2023)
- [UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images](https://mlsanity.com/paper/2402.16663/) (2024)
- [Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation](https://mlsanity.com/paper/2307.11397/) (2023)
- [UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation](https://mlsanity.com/paper/2504.21336/) (2025)
- [Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/2109.01408/) (2021)
- [Diversified and Personalized Multi-rater Medical Image Segmentation](https://mlsanity.com/paper/2403.13417/) (2024)
- [Segment Anything for Histopathology](https://mlsanity.com/paper/2502.00408/) (2025)
- [DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions](https://mlsanity.com/paper/2404.14956/) (2024)
- [Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation](https://mlsanity.com/paper/2306.03511/) (2023)
- [Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network](https://mlsanity.com/paper/2208.02912/) (2022)
- [Stable Segment Anything Model](https://mlsanity.com/paper/2311.15776/) (2023)
- [Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images](https://mlsanity.com/paper/2206.07422/) (2022)
- [Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation](https://mlsanity.com/paper/2402.04756/) (2024)
- [Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks](https://mlsanity.com/paper/2407.08625/) (2024)
- [NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation](https://mlsanity.com/paper/2408.11787/) (2024)
- [High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2412.11464/) (2024)
- [A Multi-Stage Auto-Context Deep Learning Framework for Tissue and Nuclei Segmentation and Classification in H&E-Stained Histological Images of Advanced Melanoma](https://mlsanity.com/paper/2503.23958/) (2025)
- [SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM](https://mlsanity.com/paper/2511.20027/) (2025)
- [Image Translation Based Nuclei Segmentation for Immunohistochemistry Images](https://mlsanity.com/paper/2208.06202/) (2022)
- [Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review](https://mlsanity.com/paper/2208.12460/) (2022)
- [FedSODA: Federated Cross-assessment and Dynamic Aggregation for Histopathology Segmentation](https://mlsanity.com/paper/2312.12824/) (2023)
- [MoPEFT: A Mixture-of-PEFTs for the Segment Anything Model](https://mlsanity.com/paper/2405.00293/) (2024)
- [Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation](https://mlsanity.com/paper/2501.04696/) (2025)
- [COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation](https://mlsanity.com/paper/2503.11439/) (2025)
- [PathFL: Multi-Alignment Federated Learning for Pathology Image Segmentation](https://mlsanity.com/paper/2505.22522/) (2025)
- [A Laplacian Pyramid Based Generative H&E Stain Augmentation Network](https://mlsanity.com/paper/2305.14301/) (2023)
- [Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data](https://mlsanity.com/paper/2403.03309/) (2024)

## 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.