Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences

• URL: http://arxiv.org/abs/2008.07770v1
• Date: Tue, 18 Aug 2020 07:01:24 GMT
• Title: Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences
• Authors: Xiaoran Zhang and Michelle Noga and Kumaradevan Punithakumar
• Abstract summary: We propose a fully automated approach using deep convolutional neural networks (CNN) for cardiac pathology segmentation. The input to the network consists of three CMR sequences, namely, late gadolinium enhancement (LGE), T2 and balanced steady state free precession (bSSFP) The proposed approach is evaluated by the challenge organizers with a test set including 20 cases and achieves a mean dice score of $46.8%$ for LV MS and $55.7%$ for LV ME+MS.
• Score: 0.3758535425255491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Myocardial characterization is essential for patients with myocardial infarction and other myocardial diseases, and the assessment is often performed using cardiac magnetic resonance (CMR) sequences. In this study, we propose a fully automated approach using deep convolutional neural networks (CNN) for cardiac pathology segmentation, including left ventricular (LV) blood pool, right ventricular blood pool, LV normal myocardium, LV myocardial edema (ME) and LV myocardial scars (MS). The input to the network consists of three CMR sequences, namely, late gadolinium enhancement (LGE), T2 and balanced steady state free precession (bSSFP). The proposed approach utilized the data provided by the MyoPS challenge hosted by MICCAI 2020 in conjunction with STACOM. The training set for the CNN model consists of images acquired from 25 cases, and the gold standard labels are provided by trained raters and validated by radiologists. The proposed approach introduces a data augmentation module, linear encoder and decoder module and a network module to increase the number of training samples and improve the prediction accuracy for LV ME and MS. The proposed approach is evaluated by the challenge organizers with a test set including 20 cases and achieves a mean dice score of $46.8\%$ for LV MS and $55.7\%$ for LV ME+MS

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