# NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images

arXiv: https://arxiv.org/abs/2308.01760 · HTML: https://mlsanity.com/paper/2308.01760/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [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)
- [Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification](https://mlsanity.com/paper/2108.11195/) (2021)
- [Methods for Segmentation and Classification of Digital Microscopy Tissue Images](https://mlsanity.com/paper/1810.13230/) (2018)
- [Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training](https://mlsanity.com/paper/1711.06606/) (2017)
- [CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images](https://mlsanity.com/paper/2101.00442/) (2021)
- [NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images](https://mlsanity.com/paper/2005.14511/) (2020)
- [CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting](https://mlsanity.com/paper/2303.06274/) (2023)
- [OCELOT: Overlapped Cell on Tissue Dataset for Histopathology](https://mlsanity.com/paper/2303.13110/) (2023)
- [FUSeg: The Foot Ulcer Segmentation Challenge](https://mlsanity.com/paper/2201.00414/) (2022)
- [Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types](https://mlsanity.com/paper/2002.07913/) (2020)
- [Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training](https://mlsanity.com/paper/2202.08195/) (2022)
- [Pollen Grain Microscopic Image Classification Using an Ensemble of Fine-Tuned Deep Convolutional Neural Networks](https://mlsanity.com/paper/2011.07428/) (2020)
- [A Laplacian Pyramid Based Generative H&E Stain Augmentation Network](https://mlsanity.com/paper/2305.14301/) (2023)

## Built on by (most cited first, 29 of 29)

- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation](https://mlsanity.com/paper/2408.15954/) (2024)
- [Segment Anything for Histopathology](https://mlsanity.com/paper/2502.00408/) (2025)
- [SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation](https://mlsanity.com/paper/2401.09773/) (2024)
- [Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification](https://mlsanity.com/paper/2411.07097/) (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)
- [Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data](https://mlsanity.com/paper/2403.03309/) (2024)
- [Revisiting foundation models for cell instance segmentation](https://mlsanity.com/paper/2603.17845/) (2026)
- [Unlocking adaptive digital pathology through dynamic feature learning](https://mlsanity.com/paper/2412.20430/) (2024)
- [CellStyle: Improved Zero-Shot Cell Segmentation via Style Transfer](https://mlsanity.com/paper/2503.08603/) (2025)
- [Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing](https://mlsanity.com/paper/2505.20746/) (2025)
- [NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation](https://mlsanity.com/paper/2601.20104/) (2026)
- [Towards High-Quality Image Segmentation: Improving Topology Accuracy by Penalizing Neighbor Pixels](https://mlsanity.com/paper/2603.18671/) (2026)
- [Measuring Prediction Uncertainty in Neural Cellular Automata](https://mlsanity.com/paper/2605.26726/) (2026)
- [Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization](https://mlsanity.com/paper/2309.06143/) (2023)
- [NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mouse-to-Human Domain Transfer](https://mlsanity.com/paper/2404.10130/) (2024)
- [Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis](https://mlsanity.com/paper/2412.09521/) (2024)
- [Spatial Lifting for Dense Prediction](https://mlsanity.com/paper/2507.10222/) (2025)
- [No Masks Needed: Explainable AI for Deriving Segmentation from Classification](https://mlsanity.com/paper/2508.04534/) (2025)
- [Rank-Aware Agglomeration of Foundation Models for Immunohistochemistry Image Cell Counting](https://mlsanity.com/paper/2511.12588/) (2025)
- [CellGenNet: A Knowledge-Distilled Framework for Robust Cell Segmentation in Cancer Tissues](https://mlsanity.com/paper/2511.15054/) (2025)
- [PanopMamba: Vision State Space Modeling for Nuclei Panoptic Segmentation](https://mlsanity.com/paper/2601.16631/) (2026)
- [Is SAM3 ready for pathology segmentation?](https://mlsanity.com/paper/2604.18225/) (2026)
- [NucEval: A Robust Evaluation Framework for Nuclear Instance Segmentation](https://mlsanity.com/paper/2605.03144/) (2026)
- [Count Anything](https://mlsanity.com/paper/2605.30846/) (2026)
- [HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy](https://mlsanity.com/paper/2607.04353/) (2026)
- [A Multimodal Dataset for Survival Prediction in Resected Pancreatic Ductal Adenocarcinoma](https://mlsanity.com/paper/2609.29726/) (2026)
- [BIABench: Evaluating AI agents on real-world bioimage analysis tasks](https://mlsanity.com/paper/2609.34274/) (2026)
- [Spatial Lifting for Dense Prediction](https://mlsanity.com/paper/2610.00017/) (2026)

## Abstract (arXiv)

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 at https://www.kaggle.com/datasets/ipateam/nuinsseg and https://github.com/masih4/NuInsSeg, respectively.