Attention LSTM U-Net model for Drosophila melanogaster heart tube segmentation in optical coherence microscopy images

Author:

Ouyang Xiangping1,Matt AbigailORCID,Wang Fei,Gracheva Elena,Migunova Ekaterina2,Rajamani Saathvika2,Dubrovsky Edward B.2,Zhou ChaoORCID

Affiliation:

1. Washington University in St. Louis

2. Fordham University

Abstract

Optical coherence microscopy (OCM) imaging of the Drosophila melanogaster (fruit fly) heart tube has enabled the non-invasive characterization of fly heart physiology in vivo. OCM generates large volumes of data, making it necessary to automate image analysis. Deep-learning-based neural network models have been developed to improve the efficiency of fly heart image segmentation. However, image artifacts caused by sample motion or reflections reduce the accuracy of the analysis. To improve the precision and efficiency of image data analysis, we developed an Attention LSTM U-Net model (FlyNet3.0), which incorporates an attention learning mechanism to track the beating fly heart in OCM images. The new model has improved the intersection over union (IOU) compared to FlyNet2.0 + with reflection artifacts from 86% to 89% and with movement from 81% to 89%. We also extended the capabilities of OCM analysis through the introduction of an automated, in vivo heart wall thickness measurement method, which has been validated on a Drosophila model of cardiac hypertrophy. This work will enable the comprehensive, non-invasive characterization of fly heart physiology in a high-throughput manner.

Funder

Washington University in St. Louis

Fordham University

Clayco Foundation

National Institutes of Health

Publisher

Optica Publishing Group

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