Deep Learning-Aided Automatic Contouring of Clinical Target Volumes for Radiotherapy in Breast Cancer After Modified Radical Mastectomy - You, Jinqiang; Wang, Qingxin; Wang, Ruoxi; An, Qin; Wang, Jing; Yuan, Zhiyong; Wang, Jun; Chen, Haibin; Yan, Ziye; Wei, Jun; Wang, Wei (Frontiers in Physics, 2022)
Purpose: The aim of this study is to develop a practicable automatic clinical target volume (CTV) delineation method for modified radical breast cancer surgery-based radiotherapy (MRBCRT). Methods: Unlike breast-conserving surgery, the radiotherapy CTV for modified radical BC surgery involves several regions, including CTV in the chest wall ($CTV_{cw}$), supra- and infra-clavicular region ($CTV_{sc}$), and internal mammary lymphatic region ($CTV_{im}$). For accurate and efficient segmentation of the CTVs in MRBCRT, a multi-scale convolutional neural network with an orientation attention mechanism is proposed to capture the corresponding features in different perception fields. A channel-specific local Dice loss, alongside several data augmentation methods, is also designed specifically to stabilize the model training and improve the generalization performance of the model. The segmentation performance is quantitatively evaluated by statistical metrics and qualitatively evaluated by clinicians in terms of consistency and time efficiency. Results: The proposed method is trained and evaluated on the self-collected dataset, which contains 80 computed tomography scans from patients with breast cancer who underwent modified radical breast cancer surgery. The experimental results show that the proposed segmentation method achieved superior performance in terms of DSC, HD, and ASSD compared with baseline approaches. Conclusion: Both quantitative and qualitative evaluation results demonstrated that the specifically designed method is practical and effective in automatic contouring of CTVs for MRBCRT. Clinicians can significantly save time on manual delineation while obtaining contouring results with high consistency by employing this method.