A cascaded FC-DenseNet and level set method (FCDL) for fully automatic segmentation of the right ventricle in cardiac MRI

Accurate segmentation of the right ventricle (RV) from cardiac magnetic resonance imaging (MRI) images is an essential step in estimating clinical indices such as stroke volume and ejection fraction. Recently, image segmentation methods based on fully convolutional neural networks (FCN) have drawn m...

Full description

Saved in:
Bibliographic Details
Published in:Medical & biological engineering & computing 2021-03, Vol.59 (3), p.561-574
Main Authors: Luo, Yang, Xu, Lisheng, Qi, Lin
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Computer Applications
Ground truth
Heart
Human Physiology
Image processing
Image segmentation
Imaging
Iterative methods
Magnetic resonance imaging
Neural networks
Object recognition
Original Article
Parameter robustness
Radiology
Semantic segmentation
Semantics
Stroke volume
Ventricle
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurate segmentation of the right ventricle (RV) from cardiac magnetic resonance imaging (MRI) images is an essential step in estimating clinical indices such as stroke volume and ejection fraction. Recently, image segmentation methods based on fully convolutional neural networks (FCN) have drawn much attention and shown promising results. In this paper, a new fully automatic RV segmentation method combining the FC-DenseNet and the level set method (FCDL) is proposed. The FC-DenseNet is efficiently trained end-to-end, using RV images and ground truth masks to make a per-pixel semantic inference. As a result, probability images are produced, followed by the level set method responsible for smoothing and converging contours to improve accuracy. It is noted that the iteration times of the level set method is only 4 times, which is due to the semantic segmentation of the FC-DenseNet for RV. Finally, multi-object detection algorithm is applied to locate the RV. Experimental results (including 45 cases, 15 cases for training, 30 cases for testing) show that the FCDL method outperforms the U-net + level set (UL) and the level set methods that use the same dataset and the cardiac functional parameters are computed robustly by the FCDL method. The results validate the FCDL method as an efficient and satisfactory approach to RV segmentation. Graphical abstract
ISSN:0140-0118
1741-0444
DOI:10.1007/s11517-020-02305-7