Premium
Fully automatic segmentation of 4D MRI for cardiac functional measurements
Author(s) -
Wang Yan,
Zhang Yue,
Xuan Wanling,
Kao Evan,
Cao Peng,
Tian Bing,
Ordovas Karen,
Saloner David,
Liu Jing
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13245
Subject(s) - segmentation , artificial intelligence , ventricle , image segmentation , computer science , magnetic resonance imaging , cardiac cycle , level set (data structures) , computer vision , pattern recognition (psychology) , cardiac ventricle , medicine , radiology , cardiology
Purpose Segmentation of cardiac medical images, an important step in measuring cardiac function, is usually performed either manually or semiautomatically. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV) as well as the myocardium of three‐dimensional (3D) magnetic resonance (MR) images throughout the entire cardiac cycle (four‐dimensional, 4D), remains challenging. This study proposes a deformable‐based segmentation methodology for efficiently segmenting 4D (3D + t) cardiac MR images. Methods The proposed methodology first used the Hough transform and the local Gaussian distribution method (LGD) to segment the LV endocardial contours from cardiac MR images. Following this, a novel level set‐based shape prior method was applied to generate the LV epicardial contours and the RV boundary. Results This automatic image segmentation approach has been applied to studies on 17 subjects. The results demonstrated that the proposed method was efficient compared to manual segmentation, achieving a segmentation accuracy with average Dice values of 88.62 ± 5.47%, 87.35 ± 7.26%, and 82.63 ± 6.22% for the LV endocardial, LV epicardial, and RV contours, respectively. Conclusions We have presented a method for accurate LV and RV segmentation. Compared to three existing methods, the proposed method can successfully segment the LV and yield the highest Dice value. This makes it an option for clinical assessment of the volume, size, and thickness of the ventricles.