Convolutional module for heart localization and segmentation in MRI
This work addresses the challenge of improving efficiency and accuracy in cardiac MRI analysis for medical imaging applications, representing an incremental advancement.
The paper tackled the problem of heart localization and segmentation in MRI by proposing the Visual-Motion-Focus (VMF) module, which detects heart motion and highlights regions-of-interest, resulting in a 1.7 improvement in mean Dice score for segmentation and a 2.5 times increase in training speed.
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%).