NDLI: Automatic and optimal segmentation of the left ventricle in cardiac magnetic resonance images independent of the training sets

Content Provider	IET Digital Library
Author	Wang, Zhenzhou
Abstract	In cardiac imaging, the boundary of the left ventricle (LV) could be used to measure the dyssynchrony of the heart. Hence, automatic and optimal segmentation of the LV is important. Although deep learning-based methods have achieved significant break-throughs in the accuracy of segmenting LV, it relies on a great number of training sets and the reproduction quality of the tested cases. Due to the variety of patients, it is difficult or impossible to collect the complete training sets that cover all patients with different genders, races, and ages. Therefore, methods independent of the training sets are more reliable and efficient for clinical applications. In this study, the authors propose a training sets-independent method to segment LV optimally and it outperforms all available state-of-the-art training-sets-independent image segmentation methods. In addition, they propose a framework to identify the boundary of the LV automatically. They tested these segmentation methods with both good quality and poor quality images in the proposed framework and verified that the proposed segmentation method yields the optimal solution compared to other state-of-the-art training-sets-independent segmentation methods. Based on their previous research work, the identified boundaries by the proposed approach are accurate enough for calculating the dyssynchrony of the LV.
Starting Page	1725
Ending Page	1735
Page Count	11
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 10, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5878
Journal	IET Image Processing
Publisher Date	2019-06-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Available State-of-the-art Training-sets-independent Image Segmentation Method Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Cardiac Imaging Cardiac Magnetic Resonance Image Cardiology Complete Training Sets Computer Vision And Image Processing Technique Deep Learning-based Method Image Segmentation Knowledge Engineering Technique Learning in AI Left Ventricle Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Optical, Image And Video Signal Processing Optimal Segmentation Optimal Solution Patient Diagnostic Method And Instrumentation Poor Quality Image Segment LV Optimally Segmentation Method Significant Break-throughs Spectroscopy State-of-the-art Training-sets-independent Segmentation Method Training Sets-independent Method
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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