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配准精度:多高才算足够好?纳入图像配准不确定性的统计功效计算。

Registration accuracy: how good is good enough? A statistical power calculation incorporating image registration uncertainty.

作者信息

Gibson Eli, Fenster Aaron, Ward Aaron D

机构信息

Robarts Research Institute, London, Canada.

出版信息

Med Image Comput Comput Assist Interv. 2012;15(Pt 2):643-50. doi: 10.1007/978-3-642-33418-4_79.

Abstract

Image registration is an important tool for imaging validation studies investigating the effect of underlying focal disease on the imaging signal. The strength of the conclusions drawn from these analyses is limited by statistical power. Based on the observation that in this context, statistical power depends in part on uncertainty arising from registration error, we derive a power calculation formula relating registration error, sample size, and the minimum detectable difference between normal and pathologic regions on imaging. Statistical mappings between target registration error and fractional overlap metrics are also derived, and Monte Carlo simulations are used to evaluate the derived models and test the strength of their assumptions.

摘要

图像配准是用于成像验证研究的重要工具,该研究旨在调查潜在局灶性疾病对成像信号的影响。这些分析得出的结论的力度受到统计功效的限制。基于这样的观察结果,即在这种情况下,统计功效部分取决于配准误差产生的不确定性,我们推导了一个功效计算公式,该公式涉及配准误差、样本量以及成像上正常区域与病理区域之间的最小可检测差异。还推导了目标配准误差与分数重叠指标之间的统计映射关系,并使用蒙特卡罗模拟来评估推导的模型并检验其假设的强度。

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