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Publication Detail
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration
  • Publication Type:
    Conference
  • Authors:
    Shen Z, Yang Q, Shen Y, Giganti F, Stavrinides V, Fan R, Moore C, Rusu M, Sonn G, Torr P, Barratt D, Hu Y
  • Publication date:
    18/09/2022
  • Published proceedings:
    MICCAI 2022 (25th International Conference on Medical Image Computing and Computer Assisted Intervention)
  • Name of conference:
    MICCAI 2022 (25th International Conference on Medical Image Computing and Computer Assisted Intervention)
  • Keywords:
    eess.IV, eess.IV, cs.CV
  • Notes:
    preprint version, accepted for MICCAI 2022 (25th International Conference on Medical Image Computing and Computer Assisted Intervention)
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.
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