Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration
This work addresses the problem of accurate image registration for prostate cancer patients, offering incremental improvements in a domain-specific clinical application.
The paper tackled the challenge of improving learning-based registration for longitudinal prostate MR images with limited and variable data by proposing a hierarchical quantization method and a collaborative dictionary to incorporate prior information, resulting in a 28.7% improvement in target registration error to 5.46 mm and enhanced generalization.
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.