Convolutional module for heart localization and segmentation in MRI

TL;DR

This paper introduces VMF, a convolutional module that detects heart motion in 4D MRI sequences to improve ROI detection, segmentation accuracy, and training efficiency in cardiac MRI analysis.

Contribution

The paper presents VMF, a novel module that enhances heart ROI detection and segmentation in MRI by focusing on motion estimation, leading to improved accuracy and training speed.

Findings

ROI coverage of 99.7% (Recall)

Segmentation Dice score increased by 1.7

Training speed improved by 2.5 times

Abstract

Magnetic resonance imaging (MRI) is a widely known medical imaging technique used to assess the heart function. Deep learning (DL) models perform several tasks in cardiac MRI (CMR) images with good efficacy, such as segmentation, estimation, and detection of diseases. Many DL models based on convolutional neural networks (CNN) were improved by detecting regions-of-interest (ROI) either automatically or by hand. In this paper we describe Visual-Motion-Focus (VMF), a module that detects the heart motion in the 4D MRI sequence, and highlights ROIs by focusing a Radial Basis Function (RBF) on the estimated motion field. We experimented and evaluated VMF on three CMR datasets, observing that the proposed ROIs cover 99.7% of data labels (Recall score), improved the CNN segmentation (mean Dice score) by 1.7 (p < .001) after the ROI extraction, and improved the overall training speed by 2.5 times (+150%).