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Li, L.* ; Zimmer, V.A.* ; Ding, W.* ; Wu, F.* ; Huang, L.* ; Schnabel, J.A.* ; Zhuang, X.*

Random style transfer based domain generalization networks integrating shape and spatial information.

In: (International Workshop on Statistical Atlases and Computational Models of the Heart). Berlin [u.a.]: Springer, 2021. 208-218 (Lect. Notes Comput. Sc. ; 12592 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Deep learning (DL)-based models have demonstrated good performance in medical image segmentation. However, the models trained on a known dataset often fail when performed on an unseen dataset collected from different centers, vendors and disease populations. In this work, we present a random style transfer network to tackle the domain generalization problem for multi-vendor and center cardiac image segmentation. Style transfer is used to generate training data with a wider distribution/heterogeneity, namely domain augmentation. As the target domain could be unknown, we randomly generate a modality vector for the target modality in the style transfer stage, to simulate the domain shift for unknown domains. The model can be trained in a semi-supervised manner by simultaneously optimizing a supervised segmentation and a unsupervised style translation objective. Besides, the framework incorporates the spatial information and shape prior of the target by introducing two regularization terms. We evaluated the proposed framework on 40 subjects from the M&Ms challenge2020, and obtained promising performance in the segmentation for data from unknown vendors and centers.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Domain Generalization ; Multi-center And Multi-vendor ; Random Style Transfer
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Workshop on Statistical Atlases and Computational Models of the Heart
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 12592 LNCS, Heft: , Seiten: 208-218 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)