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.
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Notes
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UK Biobank Imaging Study. http://imaging.ukbiobank.ac.uk.
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Acknowledgements
This work was supported by the EPSRC Programme Grant EP/P001009/1 and EP/R005982/1. The cardiac image dataset has been provided under UK Biobank Access Application 40119.
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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: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM 2019. Lecture Notes in Computer Science(), vol 12009. Springer, Cham. https://doi.org/10.1007/978-3-030-39074-7_20
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