Drosophila melanogaster heart tube segmentation in optical coherence tomography through an attention LSTM U-Net model

Abstract

The Drosophila Melanogaster is a powerful tool for cardiac research due to its ability for disease modeling. OCM provides cross-sectional images of its beating heart tube, which can be segmented to quantify heart parameters. Here, we expanded upon an optimized LSTM U-Net model introduced in 2023, by Fishman et al., to improve segmentation performance when artifacts are present. We incorporated attention gates via skip connections between each level of the LSTM U-Net model. This model increases the prediction intersection over union (IOU) from 0.86 to 0.89 for images with reflection artifacts and from 0.81 to 0.89 for those depicting frequent heart movement.