Research paper
Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network.
About this item
- 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...
- 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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Report this itemDigitalNZ brings together more than 30 million items from institutions so that they are easy to find and use. This information is the best information we could find on this item. This item was added on 16 May 2021, and updated 18 August 2023.
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