Yang, G;
Chen, J;
Gao, Z;
Zhang, H;
Ni, H;
Angelini, E;
Mohiaddin, R;
... Firmin, D; + view all
(2018)
Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images.
In:
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
(pp. pp. 1123-1127).
IEEE: Honolulu, Hawaii, USA.
Preview |
Text
EMBC18_1521_FI.pdf - Accepted Version Download (404kB) | Preview |
Abstract
Accurate delineation of heart substructures is a prerequisite for abnormality detection, for making quantitative and functional measurements, and for computer-aided diagnosis and treatment planning. Late Gadolinium-Enhanced Cardiac MRI (LGE-CMRI) is an emerging imaging technology for myocardial infarction or scar detection based on the differences in the volume of residual gadolinium distribution between scar and healthy tissues. While LGE-CMRI is a well-established non-invasive tool for detecting myocardial scar tissues in the ventricles, its application to left atrium (LA) imaging is more challenging due to its very thin wall of the LA and poor quality images, which may be produced because of motion artefacts and low signal-to-noise ratio. As the LGE-CMRI scan is designed to highlight scar tissues by altering the gadolinium kinetics, the anatomy among different heart substructures has less distinguishable boundaries. An accurate, robust and reproducible method for LA segmentation is highly in demand because it can not only provide valuable information of the heart function but also be helpful for the further delineation of scar tissue and measuring the scar percentage. In this study, we proposed a novel deep learning framework working on LGE-CMRI images directly by combining sequential learning and dilated residual learning to delineate LA and pulmonary veins fully automatically. The achieved results showed accurate segmentation results compared to the state-of-the-art methods. The proposed framework leads to an automatic generation of a patient-specific model that can potentially enable an objective atrial scarring assessment for the atrial fibrillation patients
| Type: | Proceedings paper |
|---|---|
| Title: | Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images |
| Event: | 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
| Location: | Honolulu, Hawaii, USA |
| Dates: | 18-21 July 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/EMBC.2018.8512550 |
| Publisher version: | https://doi.org/10.1109/EMBC.2018.8512550 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Image segmentation, Heart, Three-dimensional displays, Computer architecture, Logic gates, Myocardium |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10069441 |
Archive Staff Only
 |
View Item |
