We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.
- Authors
Wu, Kwan-Ling; Martinez-Paniagua, Melisa; Reichel, Kate; Menon, Prashant S; Deo, Shravani; Roysam, Badrinath; Varadarajan, Navin
- Abstract
Motivation Reliable 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. Results Our 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.
- Subjects
APOPTOTIC bodies; CONVOLUTIONAL neural networks; VIDEO microscopy; CELL communication
- Publication
Bioinformatics, 2023, Vol 39, Issue 10, p1
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btad584