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Open Access Green as soon as Postprint is submitted to ZB.
AtrialGeneral: Domain generalization for left atrial segmentation of multi-center LGE MRIs.
In: (International Conference on Medical Image Computing and Computer-Assisted Intervention). Berlin [u.a.]: Springer, 2021. 557-566 (Lect. Notes Comput. Sc. ; 12906 LNCS)
Left atrial (LA) segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a crucial step needed for planning the treatment of atrial fibrillation. However, automatic LA segmentation from LGE MRI is still challenging, due to the poor image quality, high variability in LA shapes, and unclear LA boundary. Though deep learning-based methods can provide promising LA segmentation results, they often generalize poorly to unseen domains, such as data from different scanners and/or sites. In this work, we collect 140 LGE MRIs from different centers with different levels of image quality. To evaluate the domain generalization ability of models on the LA segmentation task, we employ four commonly used semantic segmentation networks for the LA segmentation from multi-center LGE MRIs. Besides, we investigate three domain generalization strategies, i.e., histogram matching, mutual information based disentangled representation, and random style transfer, where a simple histogram matching is proved to be most effective.
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Publication type
Article: Conference contribution
Keywords
Atrial Fibrillation ; Domain Generalization ; Left Atrial Segmentation ; Lge Mri
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
International Conference on Medical Image Computing and Computer-Assisted Intervention
Quellenangaben
Volume: 12906 LNCS,
Pages: 557-566
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)