ArticleJournal of biophotonics2018
Segmentation of Drosophila heart in optical coherence microscopy images using convolutional neural networks.
Article in Journal of biophotonics, 2018. 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Article
- Attention LSTM U-Net model forBiomedical optics express · 2024Article
- A Drosophila heart optical coherence microscopy dataset for automatic video segmentation.Scientific data · 2023Article
- Article
- 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
- Label-free optical imaging in developmental biology [Invited].Biomedical optics express · 2020Review
- FlyNet 2.0: drosophila heart 3D (2D + time) segmentation in optical coherence microscopy images using a convolutional long short-term memory neural network.Biomedical optics express · 2020Article
- Tools to reverse-engineer multicellular systems: case studies using the fruit fly.Journal of biological engineering · 2019Review
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
Convolutional neural networks (CNNs) are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained CNN model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union of ~86%. Various morphological and dynamical cardiac parameters can be quantified accurately with automatically segmented heart regions. This study demonstrates an efficient heart segmentation method to analyze OCM images of the beating heart in Drosophila.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.