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

Optical coherence microscopy (OCM) imaging of the (fruit fly) heart tube has enabled the non-invasive characterization of fly heart physiology . 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.

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.