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Updated: Dec 25, 2025
Drosophila Preparation and Longitudinal Imaging of Heart Function In Vivo Using Optical Coherence Microscopy OCM
Published on: December 12, 2016
FlyNet 2.0: drosophila heart 3D (2D + time) segmentation in optical coherence microscopy images using a convolutional
Zhao Dong1,2, Jing Men2,3, Zhiwen Yang2,4
1Department of Electrical and Computer Engineering, Lehigh University, 27 Memorial Drive W, Bethlehem, PA 18015, USA.
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A custom convolutional neural network (CNN) integrated with convolutional long short-term memory (LSTM) achieves accurate 3D (2D + time) segmentation in cross-sectional videos of the Drosophila heart acquired by an optical coherence microscopy (OCM) system. While our previous FlyNet 1.0 model utilized regular CNNs to extract 2D spatial information from individual video frames, convolutional LSTM, FlyNet 2.0, utilizes both spatial and temporal information to improve segmentation performance further. To train and test FlyNet 2.0, we used 100 datasets including 500,000 fly heart OCM images. OCM videos in three developmental stages and two heartbeat situations were segmented achieving an intersection over union (IOU) accuracy of 92%. This increased segmentation accuracy allows morphological and dynamic cardiac parameters to be better quantified.
