Objective: Delineation of organs at risk is a time-consuming task. This study evaluates the benefits of using single-subject atlas-based automatic segmentation of organs at risk in patients with breast cancer treated in prone position, with 2 different criteria for choosing the atlas subject. Together with laterality (left/right), the criteria used were either (1) breast volume or (2) body mass index and breast cup size. Methods: An atlas supporting different selection criteria for automatic segmentation was generated from contours drawn by a senior radiation oncologist (RO_A). Atlas organs at risk included heart, left anterior descending artery, and right coronary artery. Manual contours drawn by RO_A and automatic segmentation contours of organs at risk and breast clinical target volume were created for 27 nonatlas patients. A second radiation oncologist (RO_B) manually contoured (M_B) the breast clinical target volume and the heart. Contouring times were recorded and the reliability of the automatic segmentation was assessed in the context of 3-D planning. Results: Accounting for body mass index and breast cup size improved automatic segmentation results compared to breast volume-based sampling, especially for the heart (mean similarity indexes >0.9 for automatic segmentation organs at risk and clinical target volume after RO_A editing). Mean similarity indexes for the left anterior descending artery and the right coronary artery edited by RO_A expanded by 1 cm were ≥0.8. Using automatic segmentation reduced contouring time by 40%. For each parameter analyzed (eg, D2%), the difference in dose, averaged over all patients, between automatic segmentation structures edited by RO_A and the same structure manually drawn by RO_A was <1.5% of the prescribed dose. The mean heart dose was reliable for the unedited heart segmentation, and for right-sided treatments, automatic segmentation was adequate for treatment planning with 3-D conformal tangential fields. Conclusions: Automatic segmentation for prone breast radiotherapy stratified by body mass index and breast cup size improved segmentation accuracy for the heart and coronary vessels compared to breast volume sampling. A significant reduction in contouring time can be achieved by using automatic segmentation.
Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy
