ArticleBioinformatics (Oxford, England)2023
Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Engineered small extracellular vesicles as bioactive materials: Integrating engineering strategies for cargo loading and targeted delivery systems.Bioactive materials · 2026Review
- Label-free TIMING: an efficient, reliable and scalable AI workflow for automated profiling of cell-cell interaction behaviors in nanowell arrays.Frontiers in bioinformatics · 2026Article
- Label-Free Detection of Nuclear Envelope Nucleoporation using 2D Morphological Embeddings and Machine Learning.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- The times they are AI-changing: AI-powered advances in the application of extracellular vesicles to liquid biopsy in breast cancer.Extracellular vesicles and circulating nucleic acids · 2025Review
- Apoptotic extracellular vesicles: emerging biomarkers for cancer and neurodegenerative disease diagnostics.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
motivationReliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell-cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and late indication of apoptotic onset for human melanoma cells. Our motivation is to improve the detection of apoptosis by directly detecting apoptotic bodies in a label-free manner.
resultsOur trained ResNet50 network identified nanowells containing apoptotic bodies with 92% accuracy and predicted the onset of apoptosis with an error of one frame (5 min/frame). Our apoptotic body segmentation yielded an IoU accuracy of 75%, allowing associative identification of apoptotic cells. Our method detected apoptosis events, 70% of which were not detected by Annexin-V staining. AVAILABILITY AND IMPLEMENTATION: Open-source code and sample data provided at https://github.com/kwu14victor/ApoBDproject.
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Registered trials
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