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RefineNet-based automatic delineation of the clinical target volume and organs at risk for three-dimensional
Xue Jiang1, Fang Wang1, Ying Chen1
1Department of Radiation Oncology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Background:
In recent years, high-precision image-guided intensity-modulated radiation therapy combined with three-dimensional (3D) high-dose-rate (HDR) brachytherapy (BT) has become a recommended technique for radical radiotherapy for cervical cancer. This study first employed contrast-limited adaptive histogram equalization (CLAHE) for preprocessing of input data to achieve image enhancement. In this way, rapid and accurate automatic delineation of the clinical target volume (CTV) and organs at risk (OARs) in 3D BT for cervical cancer was achieved.
Methods:
Two hundred cervical cancer patients who underwent radical radiotherapy from January 2016 to December 2018 were selected. After collecting the computed tomography (CT) image data of a patient, we constructed the radiotherapy CTV and OAR image libraries. A RefineNet-based deep learning protocol was used to segment the CTV and OARs for 3D BT for cervical cancer. In this study, a total of 1,000 rounds of training were carried out, and the model with the best performance was selected for subsequent iterative tuning. Finally, the clinical test was carried out, in which the CT images of 10 cases were tested one by one. The manual delineation results and the model output results for the CTV and OARs were compared to measure the performance of the model.
Results:
Compared with the manually delineated CTV, the RefineNet model-based segmented CTV had a higher Dice similarity coefficient (DSC), Hausdorff distance (HD), and overlap index (OI), which were 0.861, 6.005, and 0.839, respectively. For OARs, the RefineNet-based model obtained the best results for bladder segmentation (DSC: 85.96%), respectively. The mean duration of RefineNet-based automatic contour processing of the CTV was 70 s, and the mean durations of RefineNet-based automatic delineation of the bladder, rectum, sigmoid colon, and small intestine were 67, 67.4, 63.8, and 60.8 s, respectively. The total time saved by RefineNet was approximately 60%.
Conclusions:
The RefineNet-based automatic delineation model for 3D BT for cervical cancer is a stable and highly consistent automatic delineation algorithmic model that has the potential to improve the consistency of target region delineation, simplify the radiotherapy procedure, and achieve rapid and accurate automatic delineation of CTVs and OARs.
