Automatic reconstruction of interstitial needles using CT images in post-operative cervical cancer brachytherapy based on deep learning
Hongling Xie, Jiahao Wang, Yuanyuan Chen, Y. Tu, Yukai Chen, Yadong Zhao, Pengfei Zhou, Shichun Wang, Zhi‐Xin Bai, Tang Qiu
Abstract
Purpose: The purpose of this study was to investigate the precision of deep learning (DL)-based auto-reconstruction in localizing interstitial needles in post-operative cervical cancer brachytherapy (BT) using three-dimensional (3D) computed tomography (CT) images. Material and methods: A convolutional neural network (CNN) was developed and presented for automatic reconstruction of interstitial needles. Data of 70 post-operative cervical cancer patients who received CT-based BT were used to train and test this DL model. All patients were treated with three metallic needles. Dice similarity coefficient (DSC), 95% Hausdorff distance (95% HD), and Jaccard coefficient (JC) were applied to evaluate the geometric accuracy of auto-reconstruction for each needle. Dose-volume indexes (DVI) between manual and automatic methods were used to analyze the dosimetric difference. Correlation between geometric metrics and dosimetric difference was evaluated using Spearman correlation analysis. Results: > 0.05). Spearman correlation analysis demonstrated weak link between geometric metrics and dosimetry differences. Conclusions: DL-based reconstruction method can be used to precisely localize the interstitial needles in 3D-CT images. The proposed automatic approach could improve the consistency of treatment planning for post-operative cervical cancer brachytherapy.