Yang, Q; Fu, Y; Giganti, F; Ghavami, N; Chen, Q; Noble, JA; Vercauteren, T; ... Hu, Y; + view all Yang, Q; Fu, Y; Giganti, F; Ghavami, N; Chen, Q; Noble, JA; Vercauteren, T; Barratt, D; Hu, Y; - view fewer (2020) Longitudinal Image Registration with Temporal-Order and Subject-Specificity Discrimination. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. (pp. pp. 243-252). Springer Nature: Cham, Switzerland.

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Abstract

Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program. In this paper, we describe a learning-based image registration algorithm to quantify changes on regions of interest between a pair of images from the same patient, acquired at two different time points. Combining intensity-based similarity and gland segmentation as weak supervision, the population-data-trained registration networks significantly lowered the target registration errors (TREs) on holdout patient data, compared with those before registration and those from an iterative registration algorithm. Furthermore, this work provides a quantitative analysis on several longitudinal-data-sampling strategies and, in turn, we propose a novel regularisation method based on maximum mean discrepancy, between differently-sampled training image pairs. Based on 216 3D MR images from 86 patients, we report a mean TRE of 5.6 mm and show statistically significant differences between the different training data sampling strategies.

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