A Drosophila heart optical coherence microscopy dataset for automatic video segmentation

Abstract

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.

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

“…The dataset used in this study was obtained and developed from the publications by Fishman et al [ 29 ]. In brief, specimens were mounted on a glass slide, where larval and pupal specimens were mounted with double sided tape and the wings of adult flies were attached to the slide with rubber cement.…”

“…To train a generalized Drosophila heart segmentation model on OCM images, we prepared a dataset of OCM videos built off the over 600,000 frames of the open-source dataset published by Fishman et al [ 29 ]. Here, a variety of beating patterns were captured, including recordings of all developmental stages and of optogenetic experiments involving the excitatory opsins ReaChR and ChR2 and the inhibitory opsin NpHR2.0 [ 21 , 31 , 32 ].…”

“…Figure 1 presents a complete overview of the proposed heart region segmentation network. Based on the FlyNet2.0 + model [ 29 ], which consists of four convolution layers and three skip connections that bridge the corresponding encoder and decoder blocks, we constructed the FlyNet3.0 model by adding an attention gate at each skip connection, as shown in red in Fig. 1(A) .…”

“…FlyNet2.0 utilized both spatial and temporal information to improve segmentation performance, resulting in an accuracy of 92% [ 28 ]. Recently, Fishman et al optimized the FlyNet2.0 model structure with better GPU utilization and provided an open-source dataset [ 29 ]. Although these models achieved an impressive performance, there are unsatisfactory segmented cases, especially when there exist imaging artifacts originating from normal movements in the larval stage and light reflection.…”