Khan, A;
Asad, M;
Benning, M;
Roney, C;
Slabaugh, G;
(2024)
Crop and Couple: Cardiac Image Segmentation Using Interlinked Specialist Networks.
In:
Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI) 2024.
(pp. pp. 1-5).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation. We propose a novel strategy that performs segmentation using specialist networks that focus on a single anatomy (left ventricle, right ventricle, or myocardium). Given an input long-axis cardiac MR image, our method performs a ternary segmentation in the first stage to identify these anatomical regions, followed by cropping the original image to focus subsequent processing on the anatomical regions. The specialist networks are coupled through an attention mechanism that performs cross-attention to interlink features from different anatomies, serving as a soft relative shape prior. Central to our approach is an additive attention block (E-2A block), which is used throughout our architecture thanks to its efficiency. The source code is available at1.
| Type: | Proceedings paper |
|---|---|
| Title: | Crop and Couple: Cardiac Image Segmentation Using Interlinked Specialist Networks |
| Event: | 2024 IEEE International Symposium on Biomedical Imaging (ISBI) |
| Location: | Athens, Greece |
| Dates: | 27th-30th May 2024 |
| ISBN-13: | 979-8-3503-1333-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISBI56570.2024.10635217 |
| Publisher version: | http://dx.doi.org/10.1109/isbi56570.2024.10635217 |
| 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. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10197895 |
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