APSIPA Transactions on Signal and Information Processing > Vol 12 > Issue 5

A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology

Vasileios Magoulianitis, University of Southern California, USA, magoulia@usc.edu , Catherine A. Alexander, University of Southern California, USA, C.-C. Jay Kuo, University of Southern California, USA
Suggested Citation
Vasileios Magoulianitis, Catherine A. Alexander and C.-C. Jay Kuo (2024), "A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology", APSIPA Transactions on Signal and Information Processing: Vol. 12: No. 5, e203. http://dx.doi.org/10.1561/116.00000157

Publication Date: 22 Jan 2024
© 2024 V. Magoulianitis, C. A. Alexander and C.-C. J. Kuo
Molecular BiologyNuclei SegmentationWeak SupervisionSystematic Survey


Open Access

This is published under the terms of CC BY-NC.

Downloaded: 201 times

In this article:
Staining in Digital Pathology 
Methods for Nuclei Segmentation 
Evaluation and Performance Benchmarking 
Future Work 


In the cancer diagnosis pipeline, digital pathology plays an instrumental role in the identification, staging, and grading of malignant areas on biopsy tissue specimens. High resolution histology images are subject to high variance in appearance, sourcing either from the acquisition devices or the H&E staining process. Nuclei segmentation is an important task, as it detects the nuclei cells over background tissue and gives rise to the topology, size, and count of nuclei which are determinant factors for cancer detection. Yet, it is a fairly time consuming task for pathologists, with reportedly high subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern Artificial Intelligence (AI) models enable the automation of nuclei segmentation. This can reduce the subjectivity in analysis and reading time. This paper provides an extensive review, beginning from earlier works using traditional image processing techniques and reaching up to modern approaches following the Deep Learning (DL) paradigm. Our review also focuses on the weak supervision aspect of the problem, motivated by the fact that annotated data is scarce. At the end, the advantages of different models and types of supervision are thoroughly discussed. Furthermore, we try to extrapolate and envision how future research lines will potentially be, so as to minimize the need for labeled data while maintaining high performance. Future methods should emphasize efficient and explainable models with a transparent underlying process so that physicians can trust their output.



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