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Proceedings Paper

An ensemble of U-Net architecture variants for left atrial segmentation
Author(s): C. Wang; M. Rajchl; A. D. C. Chan; E. Ukwatta
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Paper Abstract

Segmentation of the left atrium and proximal pulmonary veins is an important clinical step for diagnosis of atrial fibrillation. However, the automatic segmentation of the left atrium from late gadolinium-enhanced magnetic resonance (LGE-MRI) images remains a challenging task due to differences in acquisition and large variability between individuals. Deep learning has shown to outperform traditional methodologies for segmentation in numerous tasks. A popular deep learning architecture for segmentation is the U-Net, which has shown promising results biomedical segmentation problems. Many newer network architectures have been proposed that leverage the base U-Net architecture such as attention U-Net, dense U-Net and residual U-Net. These models incorporate updated encoder blocks into the U-Net architecture to incrementally improve performance over the base U-Net. Currently, there is no comprehensive evaluation of performance between these models. In this study we (1) explore approaches for the segmentation of the left atrium based on different- Net architectures. (2) We compare and evaluate these on the STACOM 2018 Atrial Segmentation Challenge dataset and (3) ensemble these models to improve overall segmentation by reducing the internal variance between models and architectures. (4) Lastly, we define and build upon a U-Net framework to simplify development of novel U-Net inspired architectures. Our ensemble achieves a mean Dice similarity coefficient (DSC) of 92.1 ± 2.0% on a test set of twenty 3D LGE-MRI images, outperforming other fully automatic segmentation methodologies.

Paper Details

Date Published: 13 March 2019
PDF: 7 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500M (13 March 2019); doi: 10.1117/12.2512905
Show Author Affiliations
C. Wang, Carleton Univ. (Canada)
M. Rajchl, Imperial College London (United Kingdom)
A. D. C. Chan, Carleton Univ. (Canada)
E. Ukwatta, Carleton Univ. (Canada)
Univ. of Guelph (Canada)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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