A Deep Learning-Based Method with Prior Information for Auto-Delineation of Clinical Target Volume in Postmastectomy Radiotherapy

Abstract

<h3>Purpose/Objective(s)</h3> Accurately delineating clinical target volumes (CTV) is essential for radiotherapy but is time-consuming and prone to inter-observer variation. The CTV for postmastectomy radiotherapy contains several sub-regions with irregular shape and low contrast boundaries. This is challenging for automation of CTV contouring by traditional deep learning (DL) method. Thus, we designed a DL based method with prior medical information for automatic CTV delineation in postmastectomy radiotherapy. <h3>Materials/Methods</h3> A total of 61 computed tomography scans of patients received postmastectomy were collected and evaluated. CTV contains chest wall, supraclavicular region and infraclavicular region. The training/validation/test number is 35/10/16. All data are resampled, normalized in preprocessing. The proposed DL-based method includes three steps. First, the CTV are divided into four sub-regions along the vertical axis according to its morphological properties. Secondly, the location of each sub-region is determined with a 3D Unet-architecture localization network. In this step, some related organs, such as clavicle, trachea and coracoid process are used as location landmark. Thirdly, four channels are generated from located sub-regions as the input of the final segmentation network. The mean Dice similarity coefficient (DSC), 95% Hausdorff distance (95HD) and subjective evaluation were used to assess the performance of this method. <h3>Results</h3> Auto-segmentation contours demonstrated a high level of accuracy when compared with reference contours in the testing cohorts (DSC, 0.92; 95HD, 4.65mm). We compared the proposed method with conventional 2D Unet and 3D Unet models. According to Table 1, this method had a better performance for all metrics over the entire CTV. The average segmentation time for one patient's CT was less than 10 seconds using DL method. In addition, it had an improved performance at the superior, inferior slices and slices with discontinuity. The contouring accuracy of the DL-based method was comparable to that of senior radiation oncologists. The clinical experts' subjective assessments showed that more than 90% of the predicted contours were acceptable for clinical usage. <h3>Conclusion</h3> This is the first attempt that combines medical prior information and the morphological properties of CTV to DL method for postmastectomy radiotherapy. This method has a superior stable performance and improve interpretability of DL-based model.