Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets

• Published in: Medical physics (Lancaster) 2016-06, Vol.43 (6), p.2741-2755
• Main Authors: Feng, Chaolu, Zhang, Shaoxiang, Zhao, Dazhe, Li, Chunming
• Format: Article
• Language: English
• Subjects: Algorithms; Biological material, e.g. blood, urine; Haemocytometers; biomedical MRI; Cardiac dynamics; cardiac magnetic resonance image; cardiology; Clinical applications; Datasets as Topic; Diastole; Digital computing or data processing equipment or methods, specially adapted for specific applications; distance regularization; Endocardium - diagnostic imaging; feature extraction; Heart; Heart disease; Heart Ventricles - diagnostic imaging; Humans; Image data processing or generation, in general; image segmentation; Imaging, Three-Dimensional - methods; intensity inhomogeneity; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging; left ventricle segmentation; level set; Magnetic Resonance Imaging - methods; Medical image artifacts; medical image processing; Medical image segmentation; Medical magnetic resonance imaging; Muscles; Pattern Recognition, Automated - methods; Pericardium - diagnostic imaging; Regression Analysis; Segmentation; Set theory
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4947126

Purpose: 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.