Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets. Issue 6 (9th May 2016)

• Record Type: Journal Article
• Authors: Feng, Chaolu; Zhang, Shaoxiang; Zhao, Dazhe; Li, Chunming
• Is Part Of: Medical physics. Volume 43:Issue 6(2016)Part 1
• Page Start: 2741
• Page End: 2755
• Publication Date: 2016-05-09
• Subjects: biomedical MRI -- cardiology -- feature extraction -- image segmentation -- medical image processing; Clinical applications -- Cardiac dynamics -- Segmentation; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; left ventricle segmentation -- level set -- distance regularization -- intensity inhomogeneity -- cardiac magnetic resonance image; Medical image segmentation -- Heart -- Cardiac dynamics -- Heart disease -- Medical image artifacts -- Medical magnetic resonance imaging -- Set theory -- Muscles; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4947126
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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.