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Type: Conference paper
Title: A 2-step deep learning method with domain adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation
Author: Corral Acero, J.
Sundaresan, V.
Dinsdale, N.
Grau, V.
Jenkinson, M.
Citation: Lecture Notes in Artificial Intelligence, 2021, vol.12592, pp.196-207
Publisher: Springer International Publishing
Publisher Place: Cham, Switzerland
Issue Date: 2021
Series/Report no.: Lecture Notes in Computer Science; 12592
ISBN: 3030681068
ISSN: 0302-9743
Conference Name: 11th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM) (4 Oct 2020 - 4 Oct 2020 : virtual online)
Statement of
Jorge Corral Acero, Vaanathi Sundaresan, Nicola Dinsdale, Vicente Grau, Mark Jenkinson
Abstract: Segmentation of anatomical structures from Cardiac Magnetic Resonance (CMR) is central to the non-invasive quantitative assessment of cardiac function and structure. Anatomical variability, imaging heterogeneity and cardiac dynamics challenge the automation of this task. Deep learning (DL) approaches have taken over the field of automatic segmentation in recent years, however they are limited by data availability and the additional variability introduced by differences in scanners and protocols. In this work, we propose a 2-step fully automated pipeline to segment CMR images, based on DL encoder-decoder frameworks, and we explore two domain adaptation techniques, domain adversarial training and iterative domain unlearning, to overcome the imaging heterogeneity limitations. We evaluate our methods on the MICCAI 2020 Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge training and validation datasets. The results show the improvement in performance produced by domain adaptation models, especially among the seen vendors. Finally, we build an ensemble of baseline and domain adapted networks, that reported state-of-art mean Dice scores of 0.912, 0.857 and 0.861 for left ventricle (LV) cavity, LV myocardium and right ventricle cavity, respectively, on the externally validated Challenge dataset, including several unseen vendors, centers and cardiac pathologies.
Keywords: Segmentation; Cardiac magnetic resonance; Deep learning; Domain adaptation; Data harmonization
Rights: © Springer Nature Switzerland AG 2021
DOI: 10.1007/978-3-030-68107-4_20
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