Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation
Chen C, Ouyang C, Tarroni G, Schlemper J, Qiu H, Bai W, Rueckert D (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer
Book Volume: 12009 LNCS
Pages Range: 209-219
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: 9783030390730
DOI: 10.1007/978-3-030-39074-7_22
Abstract
In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire. Our framework mainly consists of two neural networks: a multi-modal image translation network for style transfer and a cascaded segmentation network for image segmentation. The multi-modal image translation network generates realistic and diverse synthetic LGE images conditioned on a single annotated bSSFP image, forming a synthetic LGE training set. This set is then utilized to fine-tune the segmentation network pre-trained on labelled bSSFP images, achieving the goal of unsupervised LGE image segmentation. In particular, the proposed cascaded segmentation network is able to produce accurate segmentation by taking both shape prior and image appearance into account, achieving an average Dice score of 0.92 for the left ventricle, 0.83 for the myocardium, and 0.88 for the right ventricle on the test set.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Chen, C., Ouyang, C., Tarroni, G., Schlemper, J., Qiu, H., Bai, W., & Rueckert, D. (2020). Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation. In Mihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 209-219). Shenzhen, CHN: Springer.
MLA:
Chen, Chen, et al. "Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation." Proceedings of the 10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Mihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra, Springer, 2020. 209-219.
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