Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training
Qiu H, Qin C, Le Folgoc L, Hou B, Schlemper J, Rueckert D (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer
Book Volume: 12009 LNCS
Pages Range: 186-194
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_20
Abstract
Deep learning based registration methods have emerged as alternatives to traditional registration methods, with competitive accuracy and significantly less runtime. Two different strategies have been proposed to train such deep learning registration networks: supervised training strategy where the model is trained to regress to generated ground truth deformation; and unsupervised training strategy where the model directly optimises the similarity between the registered images. In this work, we directly compare the performance of these two training strategies for cardiac motion estimation on cardiac cine MR sequences. Testing on real cardiac MRI data shows that while the supervised training yields more regular deformation, the unsupervised more accurately captures the deformation of anatomical structures in cardiac motion.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Qiu, H., Qin, C., Le Folgoc, L., Hou, B., Schlemper, J., & Rueckert, D. (2020). Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training. 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. 186-194). Shenzhen, CHN: Springer.
MLA:
Qiu, Huaqi, et al. "Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training." 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. 186-194.
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