ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2019
Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers.
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Who cites it
127 citing papers in PubMed.
- Image-based analysis of lymphoma cell heterogeneity: Comparative study of CRYO and FFPE techniques.Journal of pathology informatics · 2026Article
- Search, organize, aggregate and share image data with BioFile Finder (BFF).Nature methods · 2026Article
- An AI-enabled tool for quantifying overlapping red blood cell sickling dynamics in microfluidic assays.Lab on a chip · 2026Article
- Deep-learning deconvolution and segmentation of fluorescent membranes for high-precision bacterial cell-size profiling.Communications biology · 2026Article
- NucVerse3D: generalizable 3D nuclear instance segmentation across heterogeneous microscopy modalities.Scientific reports · 2026Article
- Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring.Mutagenesis · 2026Review
- Accelerated discovery of cell migration regulators using label-free deep learning-based automated tracking.Science advances · 2026Article
- Enhanced Precision of Fluorescence In Situ Hybridization (FISH) Analysis Using Neural Network-Based Nuclear Segmentation for Digital Microscopy Samples.Sensors (Basel, Switzerland) · 2026Article
- Automated 3D segmentation of human vagus nerve fascicles and epineurium from micro-computed tomography images using anatomy-aware neural networks.Journal of neural engineering · 2026Article
- Bactericidal membrane attack complex formation initiates at the new pole of E. coli.EMBO reports · 2026Article
- Image Analysis Approaches for In Situ Hybridization Technologies.Methods in molecular biology (Clifton, N.J.) · 2026Article
- An automated cell-tracking pipeline for the analysis of neutrophil dynamics.Frontiers in bioinformatics · 2026Article
- Understanding Channel Complementarity for Multiplex Cell Segmentation.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2026Article
- Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens.Cell systems · 2025Article
- Image-based identification and isolation of micronucleated cells to dissect cellular consequences.eLife · 2025Article
- Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies.Communications biology · 2025Article
- Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning.Journal of imaging · 2025Article
- Sensitive and Adaptable Turn-On Maturation (ATOM) Fluorescent Biosensors for Detecting Subcellular Localization of Protein Targets in Cells.Bio-protocol · 2025Article
- micronuclAI enables automated quantification of micronuclei for assessment of chromosomal instability.Communications biology · 2025Article
- CellBinDB: a large-scale multimodal annotated dataset for cell segmentation with benchmarking of universal models.GigaScience · 2025Article
67 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
Abstract
Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
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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.