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9 March 2020 Drosophila heart 3D segmentation using LSTM neural network in optical coherence microscopy (Conference Presentation)
Zhao Dong
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Proceedings Volume 11228, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIV; 1122823 (2020) https://doi.org/10.1117/12.2548634
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
We developed a custom convolutional neural network combined with long short-term memory , named FlyNet 2.0, to achieve accurate drosophila heart segmentation in the cross-sectional videos of drosophila heart acquired by optical coherence microscopy system. Our first version of FlyNet uses regular CNNs to only extract 2D spatial information from individual frames of drosophila heart OCM videos. LSTM utilizes both spatial and time information to further improve the drosophila heart segmentation performance. 500 thousands fly heart OCM images were employed to train and test with FlyNet 2.0. Drosophila heart OCM videos through multiple heartbeat cycles and different heartbeat dynamic recovery situations were tested with an intersection over union accuracy at 92%. With increased segmentation accuracy, dynamical cardiac parameters, like heart area, end diastolic diameter, and end systolic diameter, can be quantified more accurately for the study of drosophila cardiac disease.
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Zhao Dong "Drosophila heart 3D segmentation using LSTM neural network in optical coherence microscopy (Conference Presentation)", Proc. SPIE 11228, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIV, 1122823 (9 March 2020); https://doi.org/10.1117/12.2548634
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KEYWORDS
Heart
Image segmentation
Optical coherence microscopy
Neural networks
Video
Convolutional neural networks
In vivo imaging
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