Huang Weikai, Tang Xiaoying
Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen Guangdong China.
Healthc Technol Lett. 2021 May 2;8(3):78-83. doi: 10.1049/htl2.12011. eCollection 2021 Jun.
Large deformation diffeomorphic metric mapping for curve (LDDMM-curve) has been widely used in deformation based statistical shape analysis of the mid-sagittal corpus callosum. A main limitation of LDDMM-curve is that it is time-consuming and computationally complex. In this study, down-sampling strategies for accelerating LDDMM-curve are investigated and tested on two large datasets, one on Alzheimer's disease (155 Alzheimer's disease, 325 mild cognitive impairment and 185 healthy controls) and the other on first-episode schizophrenia (92 first-episode schizophrenia and 106 healthy controls). For both datasets a variety of down-sampling factors are tested in terms of registration accuracy, registration speed, and most importantly disease-related patterns. Experimental results revealed that down-sampling template curve by a factor of 2 can significantly reduce the running time of LDDMM-curve without sacrificing the registration accuracy. Also, the disease-induced patterns, or more specifically the group comparison results, were almost identical before and after down-sampling. It is also shown that there was no need to down-sample the target population curves but only the single template curve of the study of interest. Comprehensive analyses were conducted.
用于曲线的大变形微分同胚度量映射（LDDMM - 曲线）已广泛应用于基于变形的正中矢状胼胝体统计形状分析。LDDMM - 曲线的一个主要局限性在于它耗时且计算复杂。在本研究中，对加速LDDMM - 曲线的下采样策略进行了研究，并在两个大型数据集上进行了测试，一个数据集用于阿尔茨海默病（155例阿尔茨海默病患者、325例轻度认知障碍患者和185例健康对照），另一个数据集用于首发精神分裂症（92例首发精神分裂症患者和106例健康对照）。对于这两个数据集，在配准精度、配准速度以及最重要的疾病相关模式方面测试了多种下采样因子。实验结果表明，将模板曲线下采样2倍可显著减少LDDMM - 曲线的运行时间，同时不牺牲配准精度。此外，下采样前后疾病诱导模式，或者更具体地说，组间比较结果几乎相同。研究还表明，无需对目标人群曲线进行下采样，只需对感兴趣研究的单个模板曲线进行下采样即可。进行了综合分析。
