Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets.
OBJECTIVE
Segmentation of the cardiac left ventricle (LV) is still an open problem and is challenging due to the poor contrast between tissues around the epicardium and image artifacts. To extract the endocardium and epicardium of the cardiac left ventricle accurately, the authors propose a two-layer level set approach for segmentation of the LV from cardiac magnetic resonance short-axis images.


METHODS
In the proposed method, the endocardium and epicardium are represented by two specified level contours of a level set function. Segmentation of the LV is formulated as a problem of optimizing the level set function such that these two level contours best fit the epicardium and endocardium, subject to a distance regularization (DR) term to preserve a smoothly varying distance between them. The DR term introduces a desirable interaction between the two level contours of a single level set function, which contributes to preserve the anatomical geometry of the epicardium and endocardium of the LV. In addition, the proposed method has an intrinsic ability to deal with intensity inhomogeneity in MR images, which is a common image artifact in MRI.


RESULTS
Their method is quantitatively validated by experiments on the datasets for the MICCAI 2009 grand challenge on left ventricular segmentation and the MICCAI 2013 challenge workshop on segmentation: algorithms, theory and applications (SATA). To overcome discontinuity of 2D segmentation results at some adjacent slices for a few cases, the authors extend distance regularized two-layer level set to 3D to refine the segmentation results. The corresponding metrics for their method are better than the methods in the MICCAI 2009 challenge. Their method was ranked at the first place in terms of Hausdorff distance and the second place in terms of Dice similarity coefficient in the MICCAI 2013 challenge.


CONCLUSIONS
Experimental results demonstrate the advantages of their method in terms of segmentation accuracy and consistency with the heart anatomy.
Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning 110819, China.
Chaolu
C
Feng
Institute of Digital Medicine, Third Military Medical University, Chongqing 400038, China.
Shaoxiang
S
Zhang
School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China.
Dazhe
D
Zhao
School of Electronic Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, Sichuan Province 610051, China.
Chunming
C
Li
D000465:Algorithms; D016477:Artifacts; D066264:Datasets as Topic; D003971:Diastole; D004699:Endocardium / Q000000981:diagnostic imaging*; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D010496:Pericardium / Q000000981:diagnostic imaging*; D012044:Regression Analysis