Auto‐segmentation of the clinical target volume using a domain‐adversarial neural network in patients with gynaecological cancer undergoing postoperative vaginal brachytherapy

Abstract

Abstract Purpose For postoperative vaginal brachytherapy (POVBT), the diversity of applicators complicates the creation of a generalized auto‐segmentation model, and creating models for each applicator seems difficult due to the large amount of data required. We construct an auto‐segmentation model of POVBT using small data via domain‐adversarial neural networks (DANNs). Methods CT images were obtained postoperatively from 90 patients with gynaecological cancer who underwent vaginal brachytherapy, including 60 and 30 treated with applicators A and X, respectively. A basal model was devised using data from the patients treated with applicator A; next, a DANN model was constructed using these same 60 patients as well as 10 of those treated with applicator X through transfer learning techniques. The remaining 20 patients treated with applicator X comprised the validation set. The model's performance was assessed using objective metrics and manual clinical evaluation. Results The DANN model outperformed the basal model on both objective metrics and subjective evaluation (p<0.05 for all). The median DSC and 95HD values were 0.97 and 3.68 mm in the DANN model versus 0.94 and 5.61 mm in the basal model, respectively. Multi‐centre subjective evaluation by three clinicians showed that 99%, 98%, and 81% of CT slices contoured by the DANN model were acceptable versus only 73%, 77%, and 57% of those contoured by the basal model. One clinician deemed the DANN model comparable to manual delineation. Conclusion DANNs provides a realistic approach for the wide application of automatic segmentation of POVBT and can potentially be used to construct auto‐segmentation models from small datasets.

Keywords

• auto‐segmentation
• brachytherapy
• deep learning
• transfer learning