
Proceedings Paper
Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRIFormat | Member Price | Non-Member Price |
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Paper 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.
Paper Details
Date Published: 24 February 2017
PDF: 7 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013313 (24 February 2017); doi: 10.1117/12.2250926
Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)
PDF: 7 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013313 (24 February 2017); doi: 10.1117/12.2250926
Show Author Affiliations
Guang Yang, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
Xiahai Zhuang, Shanghai Jiao Tong Univ. (China)
Habib Khan, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
Shouvik Haldar, Royal Brompton Hospital (United Kingdom)
Eva Nyktari, Royal Brompton Hospital (United Kingdom)
Lei Li, Shanghai Jiao Tong Univ. (China)
Imperial College London (United Kingdom)
Xiahai Zhuang, Shanghai Jiao Tong Univ. (China)
Habib Khan, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
Shouvik Haldar, Royal Brompton Hospital (United Kingdom)
Eva Nyktari, Royal Brompton Hospital (United Kingdom)
Lei Li, Shanghai Jiao Tong Univ. (China)
Xujiong Ye, Univ. of Lincoln (United Kingdom)
Greg Slabaugh, City Univ. London (United Kingdom)
Tom Wong, Royal Brompton Hospital (United Kingdom)
Raad Mohiaddin, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
Jennifer Keegan, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
David Firmin, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
Greg Slabaugh, City Univ. London (United Kingdom)
Tom Wong, Royal Brompton Hospital (United Kingdom)
Raad Mohiaddin, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
Jennifer Keegan, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
David Firmin, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)
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