Development of an Automatic and Physician-Specific OAR Segmentation Framework for Radiotherapy Treatment Planning.

Purpose/Objective(s) Contouring organs at risk (OARs) is a key step of radiotherapy treatment planning. In this study, a deep learning-based framework was developed to automatize the whole OAR contouring process contributing to the clinical practice. Materials/Methods This auto-segmentation framework consisted of a pre-processing module, a deep learning-based segmentation module, and a post-processing module. We focused on the auto-segmentation in head and neck, and pelvis sites, and CT images with manual OAR contours from 67 and 54 patient cases were collected from our clinical database for the deep learning, respectively. 16 head and neck OARs and 11 pelvis OARs which were contoured in most patient cases were selected for auto-segmentation. In the pre-processing module, the original image data was extracted from the clinical DICOM Image file, and then processed for the segmentation network learning. A U-Net like network was designed in the auto-segmentation module, which was designed to consider the contour consistency among adjacent image slices and speed up the network convergency. Manual OAR contours were used as the ground truth for the network training, and a weighted Dice loss function was utilized to handle the missing organ contours in some patient cases. The segmentation results would be converted to a DCIOM Structure file in the post-processing module for treatment planning. Results The accuracy of the current auto-segmentation framework in terms of the Dice similarity coefficient (DSC) is shown in the table. Once a clinical DICOM Image data is sent to the framework, a corresponding DICOM Structure file would be automatically generated containing the OAR segmentation results. The overall operation takes about 1-2 min per case without the need for additional manual interference. Conclusion A deep learning-based framework was developed for the auto-segmentation of head and neck, and pelvis OARs with promising contouring accuracy and operation time, which is now ready to be applied in our clinical practice. Moreover, this framework is being developed toward a physician-specific framework to comply with the contour style of each radiation oncologist separately.