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Random Style Transfer Based Domain Generalization Networks Integrating Shape and Spatial Information

Li L, Zimmer VA, Ding W, Wu F, Huang L, Schnabel JA, Zhuang X (2021)

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Publication Type: Conference contribution

Publication year: 2021

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12592 LNCS

Pages Range: 208-218

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Lima, PER

ISBN: 9783030681067

DOI: 10.1007/978-3-030-68107-4_21

Abstract

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.

Involved external institutions

How to cite

APA:

Li, L., Zimmer, V.A., Ding, W., Wu, F., Huang, L., Schnabel, J.A., & Zhuang, X. (2021). Random Style Transfer Based Domain Generalization Networks Integrating Shape and Spatial Information. In Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 208-218). Lima, PER: Springer Science and Business Media Deutschland GmbH.

MLA:

Li, Lei, et al. "Random Style Transfer Based Domain Generalization Networks Integrating Shape and Spatial Information." Proceedings of the 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020, Lima, PER Ed. Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young, Springer Science and Business Media Deutschland GmbH, 2021. 208-218.

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