Data Efficient Unsupervised Domain Adaptation For Cross-modality Image Segmentation
Ouyang C, Kamnitsas K, Biffi C, Duan J, Rueckert D (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 11765 LNCS
Pages Range: 669-677
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030322441
DOI: 10.1007/978-3-030-32245-8_74
Abstract
Deep learning models trained on medical images from a source domain (e.g. imaging modality) often fail when deployed on images from a different target domain, despite imaging common anatomical structures. Deep unsupervised domain adaptation (UDA) aims to improve the performance of a deep neural network model on a target domain, using solely unlabelled target domain data and labelled source domain data. However, current state-of-the-art methods exhibit reduced performance when target data is scarce. In this work, we introduce a new data efficient UDA method for multi-domain medical image segmentation. The proposed method combines a novel VAE-based feature prior matching, which is data-efficient, and domain adversarial training to learn a shared domain-invariant latent space which is exploited during segmentation. Our method is evaluated on a public multi-modality cardiac image segmentation dataset by adapting from the labelled source domain (3D MRI) to the unlabelled target domain (3D CT). We show that by using only one single unlabelled 3D CT scan, the proposed architecture outperforms the state-of-the-art in the same setting. Finally, we perform ablation studies on prior matching and domain adversarial training to shed light on the theoretical grounding of the proposed method.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Ouyang, C., Kamnitsas, K., Biffi, C., Duan, J., & Rueckert, D. (2019). Data Efficient Unsupervised Domain Adaptation For Cross-modality Image Segmentation. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 669-677). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.
MLA:
Ouyang, Cheng, et al. "Data Efficient Unsupervised Domain Adaptation For Cross-modality Image Segmentation." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 669-677.
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