Shen, Ziyi;
Yang, Qianye;
Shen, Yuming;
Giganti, Francesco;
Stavrinides, Vasilis;
Fan, Richard;
Moore, Caroline;
... Hu, Yipeng; + view all
(2022)
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration.
In:
Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention.
MICCAI
(In press).
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Abstract
Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients. This paper describes a development in improving the learning-based registration algorithms, for this challenging clinical application often with highly variable yet limited training data. First, we report that the latent space can be clustered into a much lower dimensional space than that commonly found as bottleneck features at the deep layer of a trained registration network. Based on this observation, we propose a hierarchical quantization method, discretizing the learned feature vectors using a jointly-trained dictionary with a constrained size, in order to improve the generalisation of the registration networks. Furthermore, a novel collaborative dictionary is independently optimised to incorporate additional prior information, such as the segmentation of the gland or other regions of interest, in the latent quantized space. Based on 216 real clinical images from 86 prostate cancer patients, we show the efficacy of both the designed components. Improved registration accuracy was obtained with statistical significance, in terms of both Dice on gland and target registration error on corresponding landmarks, the latter of which achieved 5.46 mm, an improvement of 28.7% from the baseline without quantization. Experimental results also show that the difference in performance was indeed minimised between training and testing data.
| Type: | Proceedings paper |
|---|---|
| Title: | Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration |
| Event: | MICCAI 2022 (25th International Conference on Medical Image Computing and Computer Assisted Intervention) |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://conferences.miccai.org/2022/en/ |
| 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: | Registration, Quantization, Prostate Cancer |
| 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/10152405 |
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