Myocardial Segmentation of Cardiac MRI Sequences with Temporal Consistency for Coronary Artery Disease Diagnosis

TL;DR

This paper introduces a novel framework for automatic myocardial segmentation in cardiac MRI sequences that leverages temporal information to improve accuracy and consistency in diagnosing coronary artery disease.

Contribution

It proposes combining conventional and recurrent neural networks to incorporate temporal consistency in myocardial segmentation, a novel approach compared to existing methods.

Findings

Achieved up to 2% improvement in Dice coefficient.

Demonstrated better temporal consistency in segmentation results.

Validated on the ACDC dataset.

Abstract

Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial segmentation of Magnetic Resonance Imaging (MRI) sequences. As the manual segmentation is tedious, time-consuming and with low applicability, automatic myocardial segmentation using machine learning techniques has been widely explored recently. However, almost all the existing methods treat the input MRI sequences independently, which fails to capture the temporal information between sequences, e.g., the shape and location information of the myocardium in sequences along time. In this paper, we propose a myocardial segmentation framework for sequence of cardiac MRI (CMR) scanning images of left ventricular cavity, right ventricular cavity, and myocardium. Specifically, we propose to combine conventional networks and recurrent networks to incorporate temporal information between sequences to ensure temporal consistent. We evaluated our framework on the Automated Cardiac Diagnosis Challenge (ACDC) dataset. Experiment results demonstrate that our framework can improve the segmentation accuracy by up to 2% in Dice coefficient.