Prior information guided auto-contouring of breast gland for deformable image registration in postoperative breast cancer radiotherapy

Abstract

Background: Contouring of breast gland in planning CT is important to postoperative radiotherapy of patients after breast conserving surgery (BCS). However, the contouring task is difficult because of the poorer contrast of breast gland in planning CT. To improve its efficiency and accuracy, prior information was introduced in a 3D U-Net model to predict the contour of breast gland automatically. Methods: The preoperative CT was first aligned to the planning CT via affine registration. The resulting transform was then applied to the contour of breast gland in preoperative CT, and the corresponding contour in planning CT was obtained. This transformed contour was a preliminary estimation of breast gland in planning CT and was used as prior information in a 3D U-Net model to obtain a more accurate contour. For evaluation, the dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess the deep learning (DL) model's prediction accuracy. Results: The average DSC and HD of the prediction model were 0.775±0.065 and 44.979±20.565 for breast gland without the input of prior information, while the average values were 0.830±0.038 and 17.896±5.737 with the input of prior information (0.775 vs. 0.830, P=0.0014<0.05; 44.979 vs. 17.896, P=0.002<0.05). Conclusions: The prediction accuracy was increased significantly with the introduction of prior information, which provided valuable geometrical distribution of target for model training. This method provides an effective way to identify low-contrast targets from surrounding tissues in CT and will be useful in other image modalities.