Segmentation of Right Ventricle in Cardiac Cine MRI Using COLLATE Fusion-Based Multi-Atlas

doi:10.11938/cjmr20182642

Abstract

Abstract: Cardiac right ventricle (RV) segmentation plays an essential role in the functional analysis of heart diseases, such as pulmonary hypertension. The myocardium of RV is thin and irregular-shaped, making the traditional segmentation methods less effective. To improve RV segmentation, a COLLATE (Consensus Level, Labeler Accuracy and Truth Estimation) fusion-based multi-atlas method was developed. The preprocessed target image was first registered to atlas images with a B-spline algorithm optimizing normalized mutual information. The registration coefficients obtained were then used to get a rough RV segmentation for COLLATE fusion. Shape-constrained region growing algorithm was used to correct the segmentation errors. Ten cardiac magnetic resonance datasets were blindly selected to compare the performance of RV segmentation between the method developed and a method based on deep learning. The results of manual segmentation were used as the golden standard. Ejection fraction (EF) calculated with the proposed segmentation method showed better correlation and consistency with the golden standard, relative to the results calculated with the deep learning method.

Key words: cardiac right ventricle, cardiac cine magnetic resonance imaging, multi-atlas segmentation, COLLATE fusion

CLC Number:

WANG Li-jia, SU Xin-yu, LI Ya, HU Li-wei, NIE Sheng-dong. Segmentation of Right Ventricle in Cardiac Cine MRI Using COLLATE Fusion-Based Multi-Atlas[J]. Chinese Journal of Magnetic Resonance, 2018, 35(4): 407-416.

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