Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks

TL;DR

This paper presents a convolutional neural network approach for accurately segmenting the Drosophila heart in optical coherence microscopy images, enabling detailed morphological and dynamical analysis of the beating heart.

Contribution

The study introduces a novel CNN-based method for heart segmentation in OCM images, achieving high accuracy and facilitating quantitative cardiac analysis in Drosophila.

Findings

Heart regions segmented with ~86% IOU

Enables precise measurement of cardiac parameters

Demonstrates efficient analysis of beating hearts

Abstract

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.