Presentation
2 March 2022 Deep learning tissue segmentation of beating embryonic hearts from 4-D OCT images
Author Affiliations +
Abstract
Optical coherence tomography (OCT) has been applied to investigate heart development because of its capability to image both structure and function of tiny beating embryonic hearts. Labeling heart structures is necessary for quantifying mechanical functions such as cardiac motion, wall strain, blood flow and shear stress, of looping hearts. Since manual segmentation is time-consuming and labor- intensive, this study aimed to use deep learning to automatically extract dynamic shapes including the myocardium, the endocardial cushions, and the lumen of beating embryonic hearts from 4-D OCT images. This will benefit research on heart development, especially studies requiring large cohorts of embryos.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shan Ling, Jiawei Chen, Brecken Blackburn, Maryse Lapierre-Landry, Stephanie M. Ford, Michael W. Jenkins M.D., Michiko Watanabe, and Andrew M. Rollins "Deep learning tissue segmentation of beating embryonic hearts from 4-D OCT images", Proc. SPIE PC11959, Dynamics and Fluctuations in Biomedical Photonics XIX, PC1195901 (2 March 2022); https://doi.org/10.1117/12.2615082
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KEYWORDS
Heart

Image segmentation

Optical coherence tomography

Tissues

Imaging systems

Bioalcohols

Blood circulation

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