# Nuclei instance segmentation and classification in histopathology images with StarDist

arXiv: https://arxiv.org/abs/2203.02284 · HTML: https://mlsanity.com/paper/2203.02284/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 10 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification](https://mlsanity.com/paper/2108.11195/) (2021)
- [Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy](https://mlsanity.com/paper/1908.03636/) (2019)
- [CoNIC: Colon Nuclei Identification and Counting Challenge 2022](https://mlsanity.com/paper/2111.14485/) (2021)

## Built on by (most cited first, 30 of 31)

- [CellViT: Vision Transformers for Precise Cell Segmentation and Classification](https://mlsanity.com/paper/2306.15350/) (2023)
- [A co-evolving agentic AI system for medical imaging analysis](https://mlsanity.com/paper/2509.20279/) (2025)
- [Segment Anything for Histopathology](https://mlsanity.com/paper/2502.00408/) (2025)
- [Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichment](https://mlsanity.com/paper/2411.00078/) (2024)
- [PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models](https://mlsanity.com/paper/2407.09979/) (2024)
- [Circle Representation for Medical Instance Object Segmentation](https://mlsanity.com/paper/2403.11507/) (2024)
- [MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understanding](https://mlsanity.com/paper/2603.23067/) (2026)
- [SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution](https://mlsanity.com/paper/2406.09168/) (2024)
- [NuLite -- Lightweight and Fast Model for Nuclei Instance Segmentation and Classification](https://mlsanity.com/paper/2408.01797/) (2024)
- [Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification](https://mlsanity.com/paper/2310.03346/) (2023)
- [A Lightweight and Extensible Cell Segmentation and Classification Model for Whole Slide Images](https://mlsanity.com/paper/2502.19217/) (2025)
- [Segment Anything for Cell Tracking](https://mlsanity.com/paper/2509.09943/) (2025)
- [LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images](https://mlsanity.com/paper/2601.03163/) (2026)
- [NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation](https://mlsanity.com/paper/2601.20104/) (2026)
- [FG-TreeSeg: Flow-Guided Tree Crown Segmentation without Instance Annotations](https://mlsanity.com/paper/2602.00470/) (2026)
- [Bayesian Optimization for Design Parameters of 3D Image Data Analysis](https://mlsanity.com/paper/2602.15660/) (2026)
- [A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention](https://mlsanity.com/paper/2407.18673/) (2024)
- [Sequence models for continuous cell cycle stage prediction from brightfield images](https://mlsanity.com/paper/2502.02182/) (2025)
- [Cycle-Consistent Multi-Graph Matching for Self-Supervised Annotation of C.Elegans](https://mlsanity.com/paper/2503.07348/) (2025)
- [DLA-Count: Dynamic Label Assignment Network for Dense Cell Distribution Counting](https://mlsanity.com/paper/2503.12063/) (2025)
- [Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline](https://mlsanity.com/paper/2504.16979/) (2025)
- [Optimal Transport Driven Asymmetric Image-to-Image Translation for Nuclei Segmentation of Histological Images](https://mlsanity.com/paper/2506.07023/) (2025)
- [SiliCoN: Simultaneous Nuclei Segmentation and Color Normalization of Histological Images](https://mlsanity.com/paper/2506.07028/) (2025)
- [Image-Based Method For Measuring And Classification Of Iron Ore Pellets Using Star-Convex Polygons](https://mlsanity.com/paper/2506.11126/) (2025)
- [Confidence-aware agglomeration classification and segmentation of 2D microscopic food crystal images](https://mlsanity.com/paper/2507.23206/) (2025)
- [Classifying Mitotic Figures in the MIDOG25 Challenge with Deep Ensemble Learning and Rule Based Refinement](https://mlsanity.com/paper/2508.20919/) (2025)
- [Evaluating New AI Cell Foundation Models on Challenging Kidney Pathology Cases Unaddressed by Previous Foundation Models](https://mlsanity.com/paper/2510.01287/) (2025)
- [Adaptive Multi-Scale Integration Unlocks Robust Cell Annotation in Histopathology Images](https://mlsanity.com/paper/2511.13586/) (2025)
- [CellGenNet: A Knowledge-Distilled Framework for Robust Cell Segmentation in Cancer Tissues](https://mlsanity.com/paper/2511.15054/) (2025)
- [Explainable Pathomics Feature Visualization via Correlation-aware Conditional Feature Editing](https://mlsanity.com/paper/2602.05397/) (2026)

## Abstract (arXiv)

Instance segmentation and classification of nuclei is an important task in computational pathology. We show that StarDist, a deep learning nuclei segmentation method originally developed for fluorescence microscopy, can be extended and successfully applied to histopathology images. This is substantiated by conducting experiments on the Lizard dataset, and through entering the Colon Nuclei Identification and Counting (CoNIC) challenge 2022, where our approach achieved the first spot on the leaderboard for the segmentation and classification task for both the preliminary and final test phase.