Hu, Y;
Modat, M;
Gibson, E;
Ghavami, N;
Bonmati, E;
Moore, CM;
Emberton, M;
... Vercauteren, T; + view all
(2018)
Label-driven weakly-supervised learning for multimodal deformable image registration.
In:
2018 IEEE 15th International Symposium on Biomedical Imaging.
(pp. pp. 1070-1074).
IEEE
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Abstract
Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in the fixed image. These label pairs, including solid organs, ducts, vessels, point landmarks and other ad hoc structures, are only required at training time and can be spatially aligned by minimising a cross-entropy function of the warped moving label and the fixed label. During inference, the trained network takes a new image pair to predict an optimal DDF, resulting in a fully-automatic, label-free, real-time and deformable registration. For interventional applications where large global transformation prevails, we also propose a neural network architecture to jointly optimise the global- and local displacements. Experiment results are presented based on cross-validating registrations of 111 pairs of T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cance r patients with a total of over 4000 anatomical labels, yielding a median target registration error of 4.2 mm on landmark centroids and a median Dice of 0.88 on prostate glands.
| Type: | Proceedings paper |
|---|---|
| Title: | Label-driven weakly-supervised learning for multimodal deformable image registration |
| Event: | ISBI 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging, 4-7 April 2018, Washington DC, USA |
| ISBN-13: | 9781538636367 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISBI.2018.8363756 |
| Publisher version: | http://doi.org/10.1109/ISBI.2018.8363756 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | multimodal medical image registration, weakly-supervised learning, prostate cancer, glands, Biomedical imaging, Training, Image registration, Three-dimensional displays, Neural networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10051164 |
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