Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI

Yang, Guang and Zhuang, Xiahai and Khan, Habib and Haldar, Shouvik and Nyktari, Eva and Li, Lei and Ye, Xujiong and Slabaugh, Greg and Wong, Tom and Mohiaddin, Raad and Keegan, Jennifer and Firmin, David (2017) Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI. In: Proc. SPIE 10133, Medical Imaging 2017: Image Processing Conference, 11 February 2017, Orlando, Florida, United States.

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Abstract

Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is a non-invasive technique, which has shown promise in detecting native and post-ablation atrial scarring. To visualize the scarring, a precise segmentation of the left atrium (LA) and pulmonary veins (PVs) anatomy is performed as a first step—usually from an ECG gated CMRI roadmap acquisition—and the enhanced scar regions from the LGE CMRI images are superimposed. The anatomy of the LA and PVs in particular is highly variable and manual segmentation is labor intensive and highly subjective. In this paper, we developed a multi-atlas propagation based whole heart segmentation (WHS) to delineate the LA and PVs from ECG gated CMRI roadmap scans. While this captures the anatomy of the atrium well, the PVs anatomy is less easily visualized. The process is therefore augmented by semi-automated manual strokes for PVs identification in the registered LGE CMRI data. This allows us to extract more accurate anatomy than the fully automated WHS. Both qualitative visualization and quantitative assessment with respect to manual segmented ground truth showed that our method is efficient and effective with an overall mean Dice score of 0.91.

Keywords:Atlas Propagation, Multi-Scale Patch, Local Atlas Ranking, Whole Heart Segmentation, Super-Voxel, Cardiac MRI, Medical Imaging Analysis
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:26893
Deposited On:05 Apr 2017 10:37

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