  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.
