Purpose

In quantitative computed tomography (CT), manual selection of the intensity calibration phantom’s region of interest is necessary for calculating density (mg/cm³) from the radiodensity values (Hounsfield units: HU). However, as this manual process requires effort and time, the purposes of this study were to develop a system that applies a convolutional neural network (CNN) to automatically segment intensity calibration phantom regions in CT images and to test the system in a large cohort to evaluate its robustness.

Methods

This cross-sectional, retrospective study included 1040 cases (520 each from two institutions) in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used. A training dataset was created by manually segmenting the phantom regions for 40 cases (20 cases for each institution). The CNN model’s segmentation accuracy was assessed with the Dice coefficient, and the average symmetric surface distance was assessed through fourfold cross-validation. Further, absolute difference of HU was compared between manually and automatically segmented regions. The system was tested on the remaining 1000 cases. For each institution, linear regression was applied to calculate the correlation coefficients between HU and phantom density.

Results

The source code and the model used for phantom segmentation can be accessed at https://github.com/keisuke-uemura/CT-Intensity-Calibration-Phantom-Segmentation. The median Dice coefficient was 0.977, and the median average symmetric surface distance was 0.116 mm. The median absolute difference of the segmented regions between manual and automated segmentation was 0.114 HU. For the test cases, the median correlation coefficients were 0.9998 and 0.999 for the two institutions, with a minimum value of 0.9863.

Conclusion

The proposed CNN model successfully segmented the calibration phantom regions in CT images with excellent accuracy.

中文翻译：

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使用卷积神经网络自动分割临床 CT 图像中的强度校准体模

目的

在定量计算机断层扫描 (CT) 中，需要手动选择强度校准体模的感兴趣区域，以根据放射密度值（Hounsfield 单位：HU）计算密度（mg/cm ³）。然而，由于这个手动过程需要努力和时间，因此本研究的目的是开发一个系统，该系统应用卷积神经网络 (CNN) 来自动分割 CT 图像中的强度校准幻影区域，并在大量队列中测试该系统以评估其稳健性。

方法

这项横断面、回顾性研究包括 1040 个病例（两个机构各 520 个），其中使用了强度校准模型（B-MAS200，Kyoto Kagaku，Kyoto，Japan）。通过手动分割 40 个案例（每个机构 20 个案例）的幻影区域来创建训练数据集。CNN模型的分割精度通过Dice系数进行评估，平均对称面距离通过四重交叉验证进行评估。此外，比较了手动和自动分割区域之间 HU 的绝对差异。该系统在剩余的 1000 个案例中进行了测试。对于每个机构，应用线性回归来计算 HU 与幻影密度之间的相关系数。

结果

可在 https://github.com/keisuke-uemura/CT-Intensity-Calibration-Phantom-Segmentation 访问用于幻像分割的源代码和模型。骰子系数的中位数为 0.977，平均对称面距离的中位数为 0.116 毫米。手动和自动分割之间分割区域的中值绝对差异为 0.114 HU。对于测试案例，两家机构的相关系数中位数分别为0.9998和0.999，最小值为0.9863。

结论

所提出的 CNN 模型成功地分割了 CT 图像中的校准幻影区域，具有出色的准确性。