Automatic Segmentation and Cardiopathy Classification in Cardiac Mri Images Based on Deep Neural Networks

Segmentation of cardiac MRI images plays a key role in clinical diagnosis. In the traditional diagnostic process, clinical experts manually segment left ventricle (LV), right ventricle (RV) and myocardium to obtain guideline for cardiopathy diagnosis. However, manual segmentation is time-consuming a...

Full description

Saved in:
Bibliographic Details
Main Authors: Chang, Yakun, Song, Baoyu, Jung, Cheolkon, Huang, Liyu
Format: Conference Proceeding
Language:English
Subjects:
Cardiac MRI
cardiopathy diagnosis
Cavity resonators
convolutional neural networks
Diseases
Heart
Image segmentation
Magnetic resonance imaging
medical image analysis
Myocardium
Neural networks
segmentation
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Segmentation of cardiac MRI images plays a key role in clinical diagnosis. In the traditional diagnostic process, clinical experts manually segment left ventricle (LV), right ventricle (RV) and myocardium to obtain guideline for cardiopathy diagnosis. However, manual segmentation is time-consuming and labor-intensive. In this paper, we propose automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks. First, we perform object detection based on a YOLO-based network to get region of interest (ROI) from the whole sequence of diastolic and systolic MRI. Then, we obtain a pixel-wise segmentation mask automatically by feeding ROI into fully convolutional neural networks (FCN). Finally, we construct a fully connected network for cardiopathy diagnosis to decide a heart disease from the given MRI. Experimental results show that the proposed method successfully segments LV, RV and myocardium as well as achieves 90% accuracy in heart disease classification.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8461261