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arXiv · 2008.13002

Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination

Qianye Yang·Yunguan Fu·Francesco Giganti·Nooshin Ghavami·Qingchao Chen·J. Alison Noble·Tom Vercauteren·Dean Barratt·Yipeng Hu
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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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Qianye Yang, Yunguan Fu, Francesco Giganti, Nooshin Ghavami, Qingchao Chen, J. Alison Noble, Tom Vercauteren, Dean Barratt, Yipeng Hu. 2020-08-29. Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination. https://doi.org/10.1007/978-3-030-59716-0_24

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