ORCID
- Manuela Truebano: 0000-0003-2586-6524
- John I. Spicer: 0000-0002-6861-4039
- Oliver Tills: 0000-0001-8527-8383
Abstract
Delineating developmental events is central to experimental research using early life stages, permitting widespread identification of changes in event timing between species and environments. Yet, identifying developmental events is incredibly challenging, limiting the scale, reproducibility and throughput of using early life stages in experimental biology.We introduce Dev-ResNet, a small and efficient 3D convolutional neural network capable of detecting developmental events characterised by both spatial and temporal features, such as the onset of cardiac function and radula activity. We demonstrate the efficacy of Dev-ResNet using 10 diverse functional events throughout the embryonic development of the great pond snail, Lymnaea stagnalis. Dev-ResNet was highly effective in detecting the onset of all events, including the identification of thermally induced decoupling of event timings. Dev-ResNet has broad applicability given the ubiquity of bioimaging in developmental biology, and the transferability of deep learning, and so we provide comprehensive scripts and documentation for applying Dev-ResNet to different biological systems.
DOI
10.1242/jeb.247046
Publication Date
2024-05-29
Publication Title
Journal of Experimental Biology
Volume
227
Issue
10
ISSN
0022-0949
Keywords
Bioimage analysis, Computer vision, Convolutional neural networks, Heterochrony, Video classification, Animals, Developmental Biology/methods, Lymnaea/growth & development, Embryonic Development, Deep Learning
Recommended Citation
Ibbini, Z., Truebano, M., Spicer, J., McCoy, J., & Tills, O. (2024) 'Dev-ResNet: automated developmental event detection using deep learning', Journal of Experimental Biology, 227(10). Available at: https://doi.org/10.1242/jeb.247046
