Entropy Guided Unsupervised Domain Adaptation for Cross-Center Hip Cartilage Segmentation from MRI

Zeng, Guodong; Schmaranzer, Florian; Lerch, Till; Boschung, Adam; Zheng, Guoyan; Burger, Jürgen; Gerber, Kate; Tannast, Moritz; Siebenrock, Klaus-Arno; Kim, Young-Jo; Novais, Eduardo N.; Gerber, Nicolas (1 September 2020). Entropy Guided Unsupervised Domain Adaptation for Cross-Center Hip Cartilage Segmentation from MRI. Lecture notes in computer science, 12261, pp. 447-456. Springer 10.1007/978-3-030-59710-8_44

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Hip cartilage damage is a major predictor of the clinical outcome of surgical correction for femoroacetabular impingement (FAI) and hip dysplasia. Automatic segmentation for hip cartilage is an essential prior step in assessing cartilage damage status. Deep Convolutional Neural Networks have shown great success in various automated medical image segmentations, but testing on domain-shifted datasets (e.g. images obtained from different centers) can lead to severe performance losses. Creating annotations for each center is particularly expensive. Unsupervised Domain Adaptation (UDA) addresses this challenge by transferring knowledge from a domain with labels (source domain) to a domain without labels (target domain). In this paper, we propose an entropy-guided domain adaptation method to address this challenge. Specifically, we first trained our model with supervised loss on the source domain, which enables low-entropy predictions on source-like images. Two discriminators were then used to minimize the gap between source and target domain with respect to the alignment of feature and entropy distribution: the feature map discriminator

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Faculty Institutions > sitem Center for Translational Medicine and Biomedical Entrepreneurship
04 Faculty of Medicine > Department of Orthopaedic, Plastic and Hand Surgery (DOPH) > Clinic of Orthopaedic Surgery
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
04 Faculty of Medicine > Faculty Institutions > sitem Center for Translational Medicine and Biomedical Entrepreneurship > Personalised Medicine

UniBE Contributor:

Zeng, Guodong; Schmaranzer, Florian; Lerch, Till; Boschung, Adam; Burger, Jürgen; Gerber, Kate; Siebenrock, Klaus-Arno and Gerber, Nicolas

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0302-9743

ISBN:

978-3-030-59709-2

Publisher:

Springer

Language:

English

Submitter:

Guodong Zeng

Date Deposited:

20 Nov 2020 17:20

Last Modified:

09 Dec 2020 17:47

Publisher DOI:

10.1007/978-3-030-59710-8_44

BORIS DOI:

10.7892/boris.147616

URI:

https://boris.unibe.ch/id/eprint/147616

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