Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network.

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Title

Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network.

Content partner

The University of Auckland Library

Collection

ResearchSpace@Auckland

Description

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atrial structures in 3-D, especially from late gadolinium-enhanced (LGE) magnetic resonance imaging, rely heavily on manual segmentation methods that are extremely labor-intensive and prone to errors. As a result, a robust and automated method for ana...

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Format

Research paper

Research format

Journal article

Date created

2019-2

Creator

Xiong, Zhaohan / Fedorov, Vadim V / Fu, Xiaohang / Cheng, Elizabeth / Macleod, Rob / Zhao, Jichao

URL

https://hdl.handle.net/2292/55079

Related subjects

Heart Atria / Humans / Atrial Fibrillation / Gadolinium / Imaging, Three-Dimensional / Magnetic Resonance Imaging / Algorithms / Neural Networks, Computer / Science & Technology / Technology / Life Sciences & Biomedicine / Computer Science, Interdisciplinary Applications / Engineering, Biomedical / Engineering, Electrical & Electronic / Imaging Science & Photographic Technology / Radiology, Nuclear Medicine & Medical Imaging / Computer Science / Engineering / Atrial fibrillation / convolutional neural network / deep learning / MRIs / segmentation / structural analysis / FIBROSIS / IMAGES / QUANTIFICATION / MECHANISMS / VOLUME / 0801 Artificial Intelligence and Image Processing / Cardiovascular / Diagnostic Radiology / Heart Disease / 08 Information and Computing Sciences / 09 Engineering

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