FlyNet 2.0: Drosophila heart 3D (2D + time) segmentation in optical coherence microscopy images using a convolutional long short-term memory neural network

Zhao Dong, Jing Men, Zhiwen Yang, Jason Jerwick, Airong Li, Rudolph E. Tanzi, Chao Zhou

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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 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.

Original languageEnglish
Pages (from-to)1568-1579
Number of pages12
JournalBiomedical Optics Express
Volume11
Issue number3
DOIs
StatePublished - Mar 1 2020

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