Enhancing clinical efficiency and accuracy: automated segmentation for cervical cancer brachytherapy

Background: This study aims to develop an automatic segmentation model based on U-Net architecture. The model will delineate the high-risk clinical target volume (HR-CTV) and pelvic organs at risk (OARs) in brachytherapy for cervical cancer. The goal is to improve the consistency and clinical efficiency of segmentation results. Methods: The automatic segmentation model was developed by U-Net architecture, and the Computed Tomography (CT) images of 102 cervical cancer patients receiving Three-Dimensional image-guided brachytherapy (3D IGBT) were used for network training (63 cases in training set, 20 cases in test set, and 19 cases in validation set). The segmentation objects included HR-CTV, sigmoid colon, rectum, bladder and small intestine. The accuracy of the automatic segmentation model was evaluated by Dice similarity coefficient (DSC) and Hausdorff distance (HD). In addition, the time efficiency was evaluated by comparing the time of manual delineation and the time of artificial intelligence (AI) assisted automatic delineation. Results: The auto-segmentation performance of HR-CTV and OARs was good, with an average DSC of 0.90 ± 0.03 and 0.85 ± 0.04, respectively. The DSC values of the rectum, sigmoid colon and bladder were 0.91 ± 0.04, 0.88 ± 0.06 and 0.86 ± 0.04, respectively. Among the organs at risk, the small intestine had the lowest segmentation data, with a DSC of only 0.77 ± 0.20 and a HD of 25.06 ± 16.24 mm. The automatic delineation took only 1.53 ± 0.03 minutes, while the manual delineation took the longest time. AI assisted manual delineation shortened the delineation time of HR-CTV and OARs by 5 minutes and 10 minutes, respectively. Conclusions: U-Net meets clinical expectations in the delineation of HR-CTV and OARs in brachytherapy for cervical cancer, and performs better than the traditional model in HR-CTV. However, the delineation results of small intestine and sigmoid colon need to be further verified by a larger sample size.

Cervical cancer; Brachytherapy; Convolutional neural network; Automatic delineation; U-Net

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