ReviewIEEE reviews in biomedical engineering2016
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review.
Review in IEEE reviews in biomedical engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers.
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Who cites it
134 citing papers in PubMed.
- Image-based analysis of lymphoma cell heterogeneity: Comparative study of CRYO and FFPE techniques.Journal of pathology informatics · 2026Article
- Enhanced Precision of Fluorescence In Situ Hybridization (FISH) Analysis Using Neural Network-Based Nuclear Segmentation for Digital Microscopy Samples.Sensors (Basel, Switzerland) · 2026Article
- An automated cell-tracking pipeline for the analysis of neutrophil dynamics.Frontiers in bioinformatics · 2026Article
- Article
- Efficient blood cell classification from microscopic smear images using U-Net segmentation and a lightweight CNN.Scientific reports · 2025Article
- Effectiveness and user satisfaction of an online cell morphology self-study and self-test platform on blood smear diagnostic skills.BMC medical education · 2025Article
- From cells to pixels: A decision tree for designing bioimage analysis pipelines.Journal of microscopy · 2025Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data.Patterns (New York, N.Y.) · 2025Article
- Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors.Scientific reports · 2025Article
- Low-Cost Microalgae Cell Concentration Estimation in Hydrochemistry Applications Using Computer Vision.Sensors (Basel, Switzerland) · 2025Article
- A New Tool to Decrease Interobserver Variability in Biomarker Annotation in Solid Tumor Tissue for Spatial Transcriptomic Analysis.Current issues in molecular biology · 2025Article
- Scalable Nuclei Detection in HER2-SISH Whole Slide Images via Fine-Tuned Stardist with Expert-Annotated Regions of Interest.Diagnostics (Basel, Switzerland) · 2025Article
- Deep-Learning Approaches for Cervical Cytology Nuclei Segmentation in Whole Slide Images.Journal of imaging · 2025Article
- Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning.Journal of imaging · 2025Article
- Development of a deep learning-based model to evaluate changes during radiotherapy using cervical cancer digital pathology.Journal of radiation research · 2025Article
- Automated detection of c-Fos-expressing neurons using inhomogeneous background subtraction in fluorescent images.Neurobiology of learning and memory · 2025Article
- Instruction tuning for colorectal histopathology: a multimodal vision-language assistant on human-evaluated data.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- AI-augmented pathology: the experience of transfer learning and intra-domain data diversity in breast cancer metastasis detection.Frontiers in oncology · 2025Article
- ImmuNet: a segmentation-free machine learning pipeline for immune landscape phenotyping in tumors by multiplex imaging.Biology methods & protocols · 2025Article
74 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.