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Developing Drosophila melanogaster Models for Imaging and Optogenetic Control of Cardiac Function
Published on: August 25, 2022
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High-precision Drosophila heart segmentation and dynamic cardiac parameter measurement for optogenetics-OCT-based
Fei Zheng1, Renxiong Wu1, Shaoyan Huang1
1School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Journal of Biophotonics
|January 18, 2024
Summary
A new deep learning method accurately segments Drosophila hearts in optical coherence tomography images. This advances quantitative analysis of cardiac function in this important model organism.
Area of Science:
- Biomedical Engineering
- Cardiovascular Research
- Developmental Biology
Background:
- The Drosophila model is crucial for studying cardiac function.
- Optogenetics and optical coherence tomography (OCT) enable in vivo imaging of the Drosophila heart.
- Accurate, rapid quantification of cardiac parameters from OCT images is needed.
Purpose of the Study:
- To develop a deep learning method for high-precision segmentation of Drosophila heart OCT images.
- To enable accurate measurement of dynamic cardiac parameters for quantitative analysis.
- To enhance optogenetics-OCT-based cardiac function research.
Main Methods:
- Integration of U-Net and generative adversarial network architectures.
- Incorporation of residually connected convolutions for improved segmentation accuracy.
- Application to mass cross-sectional OCT images of the Drosophila heart.
Main Results:
- Achieved intersection over union and Dice similarity coefficient accuracy higher than 98%.
- Demonstrated high-precision segmentation of Drosophila heart structures in OCT images.
- Enabled accurate quantification of dynamic cardiac parameters.
Conclusions:
- The proposed deep learning method significantly improves Drosophila heart image segmentation.
- This approach enhances the efficiency and accuracy of cardiac function analysis in Drosophila models.
- Facilitates advanced optogenetics-OCT-based cardiovascular research.
