2020
DOI: 10.1364/boe.385968
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FlyNet 2.0: drosophila heart 3D (2D + time) segmentation in optical coherence microscopy images using a convolutional long short-term memory neural network

Abstract: 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 an… Show more

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Cited by 18 publications
(20 citation statements)
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References 48 publications
(53 reference statements)
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“…A vital component of the experimental success is the improved data processing with FlyNet 2.0 27 . The lab has continued to develop this software to improve both the computational efficiency and accuracy of the automated fly heart segmentation algorithm.…”
Section: Discussionmentioning
confidence: 99%
“…A vital component of the experimental success is the improved data processing with FlyNet 2.0 27 . The lab has continued to develop this software to improve both the computational efficiency and accuracy of the automated fly heart segmentation algorithm.…”
Section: Discussionmentioning
confidence: 99%
“…Segmentation of the endocardial lumen in OCT images was performed manually in this study, which is a commonly used approach and has been employed to produce the ground truths for developing automatic algorithms. 26,44,45 Recent advancements in automatic segmentation of the Drosophila cardiac lumen based on convolutional neural networks 44,45 could potentially help to reduce the time required for luminal area measurements in the early mouse heart. The measured axial resolution of OCT was lower than the theoretical value, which was likely caused by dispersion.…”
Section: Discussionmentioning
confidence: 99%
“…Innovative designs and implementations of OCT probes [ 86 , 87 ] can potentially inspire new strategies of integrating the OCT imaging head with the embryo culturing setup, which might lead to an improved experimental environment and condition for live mouse embryonic imaging. Image processing methods to extract specific features are increasingly demanded for the assessment of rich information provided by OCT, and to this end, deep-learning-enabled algorithms developed for OCT embryonic heart images [ 88 , 89 ] could have a profound impact on increasing the efficiency of analyzing the OCT cardiodynamic data. Microscopic particle image velocimetry (µPIV) is an important technique for the measurement of fluid flows; OCT-µPIV as an advanced high-resolution, depth-resolved PIV method has been demonstrated for dynamic flow imaging of chick embryos [ 90 ], which complements Doppler OCT by resolving the flow perpendicular to the OCT imaging beam, promising for robust quantification of hemodynamics in the mouse embryonic cardiovascular system.…”
Section: Perspective On Future Developmentsmentioning
confidence: 99%