Khalal D M, Azizi H, Maalej N
Laboratory of dosing, analysis and characterization in high resolution, Department of Physics, Faculty of Sciences, Ferhat Abbas Sétif 1 University, El Baz campus 19137, Sétif, Algeria.
Laboratory of dosing, analysis and characterization in high resolution, Department of Physics, Faculty of Sciences, Ferhat Abbas Sétif 1 University, El Baz campus 19137, Sétif, Algeria.
Cancer Radiother. 2023 Apr;27(2):109-114. doi: 10.1016/j.canrad.2022.08.004. Epub 2023 Feb 2.
Accurate segmentation of target volumes and organs at risk from computed tomography (CT) images is essential for treatment planning in radiation therapy. The segmentation task is often done manually making it time-consuming. Besides, it is biased to the clinician experience and subject to inter-observer variability. Therefore, and due to the development of artificial intelligence tools and particularly deep learning (DL) algorithms, automatic segmentation has been proposed as an alternative. The purpose of this work is to use a DL-based method to segment the kidneys on CT images for radiotherapy treatment planning.
In this contribution, we used the CT scans of 20 patients. Segmentation of the kidneys was performed using the U-Net model. The Dice similarity coefficient (DSC), the Matthews correlation coefficient (MCC), the Hausdorff distance (HD), the sensitivity and the specificity were used to quantitatively evaluate this delineation.
This model was able to segment the organs with a good accuracy. The obtained values of the used metrics for the kidneys segmentation, were presented. Our results were also compared to those obtained recently by other authors.
Fully automated DL-based segmentation of CT images has the potential to improve both the speed and the accuracy of radiotherapy organs contouring.
从计算机断层扫描（CT）图像中准确分割靶区体积和危及器官对于放射治疗的治疗计划至关重要。分割任务通常由人工完成，这很耗时。此外，它受临床医生经验的影响，并且存在观察者间的差异。因此，由于人工智能工具尤其是深度学习（DL）算法的发展，自动分割已被提出作为一种替代方法。这项工作的目的是使用基于DL的方法在CT图像上分割肾脏以进行放射治疗计划。
在本研究中，我们使用了20名患者的CT扫描图像。使用U-Net模型对肾脏进行分割。使用骰子相似系数（DSC）、马修斯相关系数（MCC）、豪斯多夫距离（HD）、灵敏度和特异性来定量评估这种勾画。
该模型能够以良好的准确性分割器官。给出了用于肾脏分割的所用指标的获得值。我们的结果也与其他作者最近获得的结果进行了比较。
基于DL的CT图像全自动分割有潜力提高放射治疗器官轮廓勾画的速度和准确性。
