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Algorithms for left atrial wall segmentation and thickness – Evaluation on an open-source CT and MRI image database

Abstract : Structural changes to the wall of the left atrium are known to occur with conditions that predispose to Atrial fibrillation. Imaging studies have demonstrated that these changes may be detected non-invasively. An important indicator of this structural change is the wall's thickness. Present studies have commonly measured the wall thickness at few discrete locations. Dense measurements with computer algorithms may be possible on cardiac scans of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The task is challenging as the atrial wall is a thin tissue and the imaging resolution is a limiting factor. It is unclear how accurate algorithms may get and how they compare in this new emerging area. We approached this problem of comparability with the Segmentation of Left Atrial Wall for Thickness (SLAWT) challenge organised in conjunction with MICCAI 2016 conference. This manuscript presents the algorithms that had participated and evaluation strategies for comparing them on the challenge image database that is now open-source. The image database consisted of cardiac CT (n = 10) and MRI (n = 10) of healthy and diseased subjects. A total of 6 algorithms were evaluated with different metrics, with 3 algorithms in each modality. Segmentation of the wall with algorithms was found to be feasible in both modalities. There was generally a lack of accuracy in the algorithms and inter-rater differences showed that algorithms could do better. Benchmarks were determined and algorithms were ranked to allow future algorithms to be ranked alongside the state-of-the-art techniques presented in this work. A mean atlas was also constructed from both modalities to illustrate the variation in thickness within this small cohort.
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Submitted on : Monday, November 19, 2018 - 3:11:14 PM
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Rashed Karim, Lauren-Emma Blake, Jiro Inoue, Qian Tao, Shuman Jia, et al.. Algorithms for left atrial wall segmentation and thickness – Evaluation on an open-source CT and MRI image database. Medical Image Analysis, Elsevier, 2018, 50, pp.36 - 53. ⟨10.1016/⟩. ⟨hal-01926935⟩



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