Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks
This provides an efficient method for analyzing cardiac parameters in Drosophila, but it is incremental as it applies an existing CNN approach to a new dataset.
The researchers tackled the problem of segmenting the heart region in Drosophila optical coherence microscopy images using a convolutional neural network, achieving an intersection over union of ~86% for marking heart regions across multiple heartbeat cycles.
Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dynamical cardiac parameters can be quantified accurately with automatically segmented heart regions. This study demonstrates an efficient heart segmentation method to analyze OCM images of the beating heart in Drosophila.