基于时序和主体特异性判别的纵向图像配准
Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination
摘要：
纵向MR图像的形态分析在监测前列腺癌患者的疾病进展中起着关键作用，前列腺癌患者被置于积极的监测计划之下。在本文中，我们描述了一种基于学习的图像配准算法来量化在两个不同时间点采集的同一患者的一对图像之间的感兴趣区域的变化。结合基于强度的相似性和腺体分割作为弱监督，与配准前和迭代配准算法相比，人口数据训练的配准网络显着降低了保留患者数据的目标配准误差（TREs）。此外，这项工作提供了一个定量分析的几个原始数据采样策略，反过来，我们提出了一种新的正则化方法的基础上最大的平均差异，不同采样的训练图像对之间。基于来自86名患者的216张3D MR图像，我们报告了平均TRE为5.6 mm，并且显示了不同训练数据采样策略之间的统计学显著差异。
英文摘要：
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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