Convolutional module for heart localization and segmentation in MRI

Autor:	Lima, Daniel, Graves, Catharine, Gutierrez, Marco, Brandoli, Bruno, Rodrigues-Jr, Jose
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition 68T07 92C55 92B20 I.2.10 I.4.6 I.5.1 J.3
Zdroj:	ICIAP 2022, Part I, LNCS 13231
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-031-06427-2_37
Popis:	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%). Comment: Submitted to CMIG
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2107.09134 Zobrazit plný text záznamu View this record from Arxiv