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Deep learning applications in automatic segmentation and reconstruction in CT-based cervix brachytherapy
Journal of Contemporary Brachytherapy ( IF 1.1 ) Pub Date : 2021-05-13 , DOI: 10.5114/jcb.2021.106118 Hai Hu 1, 2 , Qiang Yang 1, 2 , Jie Li 2 , Pei Wang 2 , Bin Tang 2 , Xianliang Wang 2 , Jinyi Lang 2
Journal of Contemporary Brachytherapy ( IF 1.1 ) Pub Date : 2021-05-13 , DOI: 10.5114/jcb.2021.106118 Hai Hu 1, 2 , Qiang Yang 1, 2 , Jie Li 2 , Pei Wang 2 , Bin Tang 2 , Xianliang Wang 2 , Jinyi Lang 2
Affiliation
Introduction
Motivated by recent advances in deep learning, the purpose of this study was to investigate a deep learning method in automatic segment and reconstruct applicators in computed tomography (CT) images for cervix brachytherapy treatment planning.
Material and methods
U-Net model was developed for applicator segmentation in CT images. Sixty cervical cancer patients with Fletcher applicator were divided into training data and validation data according to ratio of 50 : 10, and another 10 patients with Fletcher applicator were employed to test the model. Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used to evaluate the model. Segmented applicator coordinates were calculated and applied into RT structure file. Tip error and shaft error of applicators were evaluated. Dosimetric differences between manual reconstruction and deep learning-based reconstruction were compared.
Results
The averaged overall 10 test patients’ DSC, HD95, and reconstruction time were 0.89, 1.66 mm, and 17.12 s, respectively. The average tip error was 0.80 mm, and the average shaft error was less than 0.50 mm. The dosimetric differences between manual reconstruction and automatic reconstruction were 0.29% for high-risk clinical target volume (HR-CTV) D90%, and less than 2.64% for organs at risk D2cc at a scenario of doubled maximum shaft error.
Conclusions
We proposed a deep learning-based reconstruction method to localize Fletcher applicator in three-dimensional CT images. The achieved accuracy and efficiency confirmed our method as clinically attractive. It paves the way for the automation of brachytherapy treatment planning.
中文翻译：
深度学习在基于CT的子宫颈近距离放射治疗的自动分割和重建中的应用
简介
受到深度学习最新进展的推动，本研究的目的是研究自动分段的深度学习方法，并在计算机断层扫描（CT）图像中重建应用器以进行子宫颈近距离放射治疗计划。
材料与方法
开发了用于在CT图像中进行涂药器分割的U-Net模型。按照50:10的比例将60例带有Fletcher涂药器的宫颈癌患者分为训练数据和验证数据，另有10例Fletcher涂药器的患者用于模型检验。使用骰子相似系数（DSC）和95％的Hausdorff距离（HD95）评估模型。计算分段的施涂器坐标，并将其应用到RT结构文件中。评估了涂药器的尖端误差和轴误差。比较了手动重建和基于深度学习的重建之间的剂量差异。
结果
10名测试患者的平均平均DSC，HD95和重建时间分别为0.89、1.66 mm和17.12 s。平均叶尖误差为0.80毫米，平均轴误差小于0.50毫米。在最大轴误差加倍的情况下，高风险临床目标体积（HR-CTV）D90％的手动重建与自动重建之间的剂量学差异为0.29％，而对于风险为D2cc的器官而言，其差异小于2.64％。
结论
我们提出了一种基于深度学习的重建方法来在三维CT图像中定位Fletcher涂抹器。达到的准确性和效率证实了我们的方法在临床上具有吸引力。它为近距离放射治疗计划的自动化铺平了道路。
更新日期：2021-05-26
Motivated by recent advances in deep learning, the purpose of this study was to investigate a deep learning method in automatic segment and reconstruct applicators in computed tomography (CT) images for cervix brachytherapy treatment planning.
Material and methods
U-Net model was developed for applicator segmentation in CT images. Sixty cervical cancer patients with Fletcher applicator were divided into training data and validation data according to ratio of 50 : 10, and another 10 patients with Fletcher applicator were employed to test the model. Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used to evaluate the model. Segmented applicator coordinates were calculated and applied into RT structure file. Tip error and shaft error of applicators were evaluated. Dosimetric differences between manual reconstruction and deep learning-based reconstruction were compared.
Results
The averaged overall 10 test patients’ DSC, HD95, and reconstruction time were 0.89, 1.66 mm, and 17.12 s, respectively. The average tip error was 0.80 mm, and the average shaft error was less than 0.50 mm. The dosimetric differences between manual reconstruction and automatic reconstruction were 0.29% for high-risk clinical target volume (HR-CTV) D90%, and less than 2.64% for organs at risk D2cc at a scenario of doubled maximum shaft error.
Conclusions
We proposed a deep learning-based reconstruction method to localize Fletcher applicator in three-dimensional CT images. The achieved accuracy and efficiency confirmed our method as clinically attractive. It paves the way for the automation of brachytherapy treatment planning.
中文翻译：
深度学习在基于CT的子宫颈近距离放射治疗的自动分割和重建中的应用
简介
受到深度学习最新进展的推动，本研究的目的是研究自动分段的深度学习方法，并在计算机断层扫描（CT）图像中重建应用器以进行子宫颈近距离放射治疗计划。
材料与方法
开发了用于在CT图像中进行涂药器分割的U-Net模型。按照50:10的比例将60例带有Fletcher涂药器的宫颈癌患者分为训练数据和验证数据，另有10例Fletcher涂药器的患者用于模型检验。使用骰子相似系数（DSC）和95％的Hausdorff距离（HD95）评估模型。计算分段的施涂器坐标，并将其应用到RT结构文件中。评估了涂药器的尖端误差和轴误差。比较了手动重建和基于深度学习的重建之间的剂量差异。
结果
10名测试患者的平均平均DSC，HD95和重建时间分别为0.89、1.66 mm和17.12 s。平均叶尖误差为0.80毫米，平均轴误差小于0.50毫米。在最大轴误差加倍的情况下，高风险临床目标体积（HR-CTV）D90％的手动重建与自动重建之间的剂量学差异为0.29％，而对于风险为D2cc的器官而言，其差异小于2.64％。
结论
我们提出了一种基于深度学习的重建方法来在三维CT图像中定位Fletcher涂抹器。达到的准确性和效率证实了我们的方法在临床上具有吸引力。它为近距离放射治疗计划的自动化铺平了道路。
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