Accessible deep learning in the cancer clinic: an open-source, prototype framework for automatic contouring

With the rapid growth in deep learning research for medical applications, the value of making these techniques accessible to clinics also increases. Many medical technology companies now offer deep learning contouring, but researchers are usually limited to the proprietary pre-trained models. To fully explore the technology, researchers must build deep learning pipelines from scratch. We developed an open-source framework for producing automatic contours for 11 common organs-at-risk (OAR) for head and neck planning CT studies using a convolutional neural network (CNN). The pipeline handles DICOM file ingestion, data pre-processing, CNN utilization, output postprocessing, and DICOM structure set file creation to allow end-to-end use interfacing directly with DICOM files. We trained a standard U-Net model on 210 anonymized head and neck patients from our clinic, validated the model's performance on a test set of 19 patients, and provide the pre- trained weights as a part of the pipeline offering to allow for immediate use. Scripts for retraining the model are also provided to allow customization and new research efforts. Additionally, we offer a framework of all necessary files to support browser- based, no-code contour generation using the Flask package for Python. These contributions lay the foundation for clinical workflow integration. All files are freely available in a public GitHub repository (https://github.com/jasbach/HN_UNet_Autosegmentation_Tool) and are ready for immediate use. Our work offers a demonstrably successful deep learning tool for automatic contouring with a reduced barrier to entry for novice personnel wishing to expand their efforts into the discipline.