Myocardial segmentation in cardiac magnetic resonance images using fully convolutional neural networks. (July 2018)

• Record Type: Journal Article
• Authors: Romaguera, Liset Vázquez; Romero, Francisco Perdigón; Fernandes Costa Filho, Cicero Ferreira; Fernandes Costa, Marly Guimarães
• Is Part Of: Biomedical signal processing and control. Volume 44(2018)
• Page Start: 48
• Page End: 57
• Publication Date: 2018-07
• Subjects: Left ventricle -- Deep learning -- Fully convolutional neural networks -- Cardiac MRI -- Segmentation; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.04.008
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: According to the World Health Organization, cardiovascular diseases are the leading cause of death worldwide. Many coronary diseases involve the left ventricle; therefore, estimation of several functional parameters from a previous segmentation of this structure can be helpful in diagnosis. Although a high number of automated methods have been proposed, left ventricle segmentation in cardiac MRI images remains an open problem. In this work we propose a deep fully convolutional neural network architecture to address this issue and assess its performance. The model was trained end to end in a supervised learning stage from whole image input and ground truths to make a per pixel classification in order to segment the myocardium. For its design, development and experimentation a Caffe deep learning framework over an NVidia Quadro K4200 Graphics Processing Unit was used. Training and testing processes were carried out using 10-fold cross validation with short axis images. In addition, the performance of six optimization methods was compared. The proposed model was validated in 45 datasets of Sunnybrook database using a Dice coefficient, Average Perpendicular Distance (APD) and percentage of good contours (GC) metrics and compared with other state-of-the-art approaches. Results show the robustness and feasibility of the proposed method.