A Drosophila heart optical coherence microscopy dataset for automatic video segmentation

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

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.

A Drosophila heart optical coherence microscopy dataset for automatic video segmentation

The heart of the fruit fly, Drosophila melanogaster, is a particularly suitable model for cardiac studies. Optical coherence microscopy (OCM) captures in vivo cross-sectional videos of the beating Drosophila heart for cardiac function quantification. To analyze those large-size multi-frame OCM recordings, human labelling has been employed, leading to low efficiency and poor reproducibility. Here, we introduce a robust and accurate automated Drosophila heart segmentation algorithm, called FlyNet 2.0+, which utilizes a long short-term memory (LSTM) convolutional neural network to leverage time series information in the videos, ensuring consistent, high-quality segmentation. We present a dataset of 213 Drosophila heart videos, equivalent to 604,000 cross-sectional images, containing all developmental stages and a wide range of beating patterns, including faster and slower than normal beating, arrhythmic beating, and periods of heart stop to capture these heart dynamics. Each video contains a corresponding ground truth mask. We expect this unique large dataset of the beating Drosophila heart in vivo will enable new deep learning approaches to efficiently characterize heart function to advance cardiac research.