Yang, Qianye;
Atkinson, David;
Fu, Yunguan;
Syer, Tom;
Yan, Wen;
Punwani, shonit;
Clarkson, Matthew;
... Hu, Yipeng; + view all
(2022)
Cross-Modality Image Registration using a Training-Time Privileged Third Modality.
IEEE Transactions on Medical Imaging
, 41
(11)
pp. 3421-3431.
10.1109/TMI.2022.3187873.
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Abstract
— In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusionweighted scans with high b-value (DWI_{high−b}). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI_{b=0}) are considered easier to register to T2w due to the availability of corresponding features. We propose a learning from privileged modality algorithm, using a training-only imaging modality DWIb=0, to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients and report, with statistical significance, a lowered median target registration error of 4.34 mm, when registering the holdout DWI_{high−b} and T2w image pairs, compared with that of 7.96 mm before registration. Results also show that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and other tested learning-based methods with/without the additional modality. These compared algorithms also failed to produce any significantly improved alignment between DWI_{high−b} and T2w in this challenging application.
| Type: | Article |
|---|---|
| Title: | Cross-Modality Image Registration using a Training-Time Privileged Third Modality |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TMI.2022.3187873 |
| Publisher version: | https://doi.org/10.1109/TMI.2022.3187873 |
| 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: | Medical image registration, Privileged learning, Deep learning, Multi-parametric MRI |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10151759 |
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