ArticleBiomedical optics express2020
FlyNet 2.0: drosophila heart 3D (2D + time) segmentation in optical coherence microscopy images using a convolutional long short-term memory neural network.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Article
- Dual color optogenetic tool enables heart arrest, bradycardic, and tachycardic pacing in Drosophila melanogaster.Communications biology · 2024Article
- Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning.Communications biology · 2024Article
- Attention LSTM U-Net model forBiomedical optics express · 2024Article
- A Drosophila heart optical coherence microscopy dataset for automatic video segmentation.Scientific data · 2023Article
- Article
- Developing Drosophila melanogaster Models for Imaging and Optogenetic Control of Cardiac Function.Journal of visualized experiments : JoVE · 2022Article
- Embryonic Mouse Cardiodynamic OCT Imaging.Journal of cardiovascular development and disease · 2020Review
- Live mechanistic assessment of localized cardiac pumping in mammalian tubular embryonic heart.Journal of biomedical optics · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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Registered trials
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