Yoon Sung Hyun, Kim Jihang, Lee Kyong Joon, Nam Chang-Mo, Kim Junghoon, Lee Kyung Hee, Lee Kyung Won
Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.
Monitor Corporation, Seoul, Korea.
Quant Imaging Med Surg. 2022 Mar;12(3):1674-1683. doi: 10.21037/qims-21-485.
When assessing the volume of pulmonary nodules on computed tomography (CT) images, there is an inevitable discrepancy between values based on the diameter-based volume calculation and the voxel-counting method, which is derived from the Euclidean distance measurement method on pixel/voxel-based digital image. We aimed to evaluate the ability of a modified diameter measurement method to reduce the discrepancy, and we determined a conversion equation to equate volumes derived from different methods.
Two different anthropomorphic phantoms with subsolid and solid nodules were repeatedly scanned under various settings. Nodules in CT images were detected and segmented using a fully automated algorithm and the volume was calculated using three methods: the voxel-counting method ( ), diameter-based volume calculation ( ), and a modified diameter-based volume calculation ( ), in which one pixel spacing was added to the diameters in the three axes (x-, y-, and z-axis). For each nodule, and were compared to by computing the absolute percentage error (APE) as follows: =100 × ( - )/ . Comparisons between APE and APE according to CT parameter setting were performed using the Wilcoxon signed-rank test. The Jonckheere-Terpstra test was used to evaluate trends across the four different nodule sizes.
The deep learning-based computer-aided diagnosis (DL-CAD) successfully detected and segmented all nodules in a fully automatic manner. The APE was significantly less with than with (Wilcoxon signed-rank test, P<0.05) regardless of CT parameters and nodule size. The APE median increased as the size of the nodule decreased. This trend was statistically significant (Jonckheere-Terpstra test, P<0.001) regardless of volume measurement method (diameter-based and modified diameter-based volume calculations).
Our modified diameter-based volume calculation significantly reduces the discrepancy between the diameter-based volume calculation and voxel-counting method.
在计算机断层扫描（CT）图像上评估肺结节体积时，基于直径的体积计算值与体素计数法得出的值之间存在不可避免的差异，体素计数法源自基于像素/体素的数字图像上的欧几里得距离测量方法。我们旨在评估一种改良的直径测量方法减少这种差异的能力，并确定一个转换方程，使不同方法得出的体积相等。
在各种设置下对两个具有亚实性和实性结节的不同人体模型进行反复扫描。使用全自动算法检测并分割CT图像中的结节，并使用三种方法计算体积：体素计数法（ ）、基于直径的体积计算法（ ）和改良的基于直径的体积计算法（ ），其中在三个轴（x轴、y轴和z轴）的直径上增加一个像素间距。对于每个结节，通过计算绝对百分比误差（APE）将 和 与 进行比较： =100×( - )/ 。使用Wilcoxon符号秩检验对根据CT参数设置的APE和APE之间进行比较。使用Jonckheere-Terpstra检验评估四种不同结节大小的趋势。
基于深度学习的计算机辅助诊断（DL-CAD）以全自动方式成功检测并分割了所有结节。无论CT参数和结节大小如何， 得出的APE均显著低于 （Wilcoxon符号秩检验，P<0.05）。APE中位数随着结节尺寸减小而增加。无论体积测量方法（基于直径和改良的基于直径的体积计算）如何，这种趋势均具有统计学意义（Jonckheere-Terpstra检验，P<0.001）。
我们改良的基于直径的体积计算显著减少了基于直径的体积计算与体素计数法之间的差异。
