Multi-modal registration, especially CT/MR to ultrasound (US), is still a challenge, as conventional similarity metrics such as mutual information do not match the imaging characteristics of ultrasound. The main motivation for this work is to investigate whether a deep learning network can be used to directly estimate the displacement between a pair of multi-modal image patches, without explicitly performing similarity metric and optimizer, the two main components in a registration framework. The proposed DVNet is a fully convolutional neural network and is trained using a large set of artificially generated displacement vectors (DVs). The DVNet was evaluated on mono- and simulated multi-modal data, as well as real CT and US liver slices (selected from 3D volumes). The results show that the DVNet is quite robust on the single- and multi-modal (simulated) data, but does not work yet on the real CT and US images.

Additional Metadata
Keywords CNN, CT, Liver, Registration, Ultrasound
Persistent URL dx.doi.org/10.1007/978-3-030-02628-8_5, hdl.handle.net/1765/112185
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Citation
Sun, Y. (Yuanyuan), Moelker, A, Niessen, W.J, & van Walsum, T.W. (2018). Towards robust CT-ultrasound registration using deep learning methods. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). doi:10.1007/978-3-030-02628-8_5