Sakashita Naohiro, Shirai Kiyonori, Ueda Yoshihiro, Ono Ayuka, Teshima Teruki
Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, Osaka, Japan.
Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan.
Rep Pract Oncol Radiother. 2020 Nov-Dec;25(6):981-986. doi: 10.1016/j.rpor.2020.09.005. Epub 2020 Oct 2.
This study evaluated a convolutional neural network (CNN) for automatically delineating the liver on contrast-enhanced or non-contrast-enhanced CT, making comparisons with a commercial automated technique (MIM Maestro®).
Intensity-modulated radiation therapy requires careful labor-intensive planning involving delineation of the target and organs on CT or MR images to ensure delivery of the effective dose to the target while avoiding organs at risk.
Contrast-enhanced planning CT images from 101 pancreatic cancer cases and accompanying mask images showing manually-delineated liver contours were used to train the CNN to segment the liver. The trained CNN then performed liver segmentation on a further 20 contrast-enhanced and 15 non-contrastenhanced CT image sets, producing three-dimensional mask images of the liver.
For both contrast-enhanced and non-contrast-enhanced images, the mean Dice similarity coefficients between CNN segmentations and ground-truth manual segmentations were significantly higher than those between ground-truth and MIM Maestro software (p < 0.001). Although mean CT values of the liver were higher on contrast-enhanced than on non-contrast-enhanced CT, there were no significant differences in the Hausdorff distances of the CNN segmentations, indicating that the CNN could successfully segment the liver on both image types, despite being trained only on contrast-enhanced images.
Our results suggest that a CNN can perform highly accurate automated delineation of the liver on CT images, irrespective of whether the CT images are contrast-enhanced or not.
本研究评估了一种卷积神经网络（CNN）在对比增强或非对比增强CT上自动勾画肝脏的能力，并与一种商业自动化技术（MIM Maestro®）进行比较。
调强放射治疗需要精心的、耗费人力的计划，包括在CT或MR图像上勾画靶区和器官，以确保在避免危及器官的同时将有效剂量传递至靶区。
使用来自101例胰腺癌病例的对比增强计划CT图像以及显示手动勾画肝脏轮廓的配套掩码图像来训练CNN以分割肝脏。然后，经过训练的CNN对另外20组对比增强CT图像和15组非对比增强CT图像进行肝脏分割，生成肝脏的三维掩码图像。
对于对比增强图像和非对比增强图像，CNN分割结果与手动分割真值之间的平均骰子相似系数均显著高于真值与MIM Maestro软件之间的平均骰子相似系数（p < 0.001）。尽管肝脏在对比增强CT上的平均CT值高于非对比增强CT，但CNN分割结果的豪斯多夫距离并无显著差异，这表明尽管CNN仅在对比增强图像上进行训练，但它能够成功地在两种图像类型上分割肝脏。
我们的结果表明，无论CT图像是否为对比增强图像，CNN都能在CT图像上对肝脏进行高度准确的自动勾画。
