mlsanityData to 14 Oct 2026

Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review

arXiv 2208.12460 ↗First posted 26 Aug 2022eess.IV cs.CVcited by 3

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Abstract (arXiv)

Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand challenges have seen a surge in automated methods focusing on domain specific challenges, which is pivotal for technology advancements and clinical translation. In this survey, 126 papers illustrating the AI based methods for nuclei and glands instance segmentation published in the last five years (2017-2022) are deeply analyzed, the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and a detailed insights on the grand challenges illustrating the top performing methods specific to each challenge is also provided. Besides, we intended to give the reader current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 39 of its 168 references that are in the map. First posted on arXiv 26 Aug 2022.

Builds on

39 of 168 references.

References · 39

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27 more
  1. 2018MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Imagescited by 87
  2. 2021GANs for Medical Image Synthesis: An Empirical Studycited by 65
  3. 2018Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Imagescited by 56
  4. 2020Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Imagescited by 55
  5. 2019CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregationcited by 54
  6. 2020Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistencycited by 52
  7. 2021CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Imagescited by 51
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  11. 2021NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationcited by 34
  12. 2020Self-Supervised Nuclei Segmentation in Histopathological Images Using Attentioncited by 30
  13. 2019PseudoEdgeNet: Nuclei Segmentation only with Point Annotationscited by 27
  14. 2020Instance-aware Self-supervised Learning for Nuclei Segmentationcited by 27
  15. 2019Mask-RCNN and U-net Ensembled for Nuclei Segmentationcited by 23
  16. 2018A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Imagescited by 15
  17. 2020Pairwise Relation Learning for Semi-supervised Gland Segmentationcited by 13
  18. 2021NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scalecited by 12
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  20. 2019Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Frameworkcited by 10
  21. 2020Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Imagescited by 10
  22. 2021SRPN: similarity-based region proposal networks for nuclei and cells detection in histology imagescited by 6
  23. 2018Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Modelcited by 3
  24. 2019US-net for robust and efficient nuclei instance segmentationcited by 3
  25. 2019Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Featurescited by 2
  26. 2020Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRIcited by 2
  27. 2018Clumped Nuclei Segmentation with Adjacent Point Match and Local Shape based Intensity Analysis for Overlapped Nuclei in Fluorescence In-Situ Hybridization Imagescited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichmentcited by 5
  2. 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
  3. 2025LG-NuSegHop: A Local-to-Global Self-Supervised Pipeline For Nuclei Instance Segmentationcited by 0