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

John Asbach; Anurag Singh; Mark Farrugia; L. Shawn Matott; Anh Le

doi:10.1117/12.2611624

4 April 2022 Accessible deep learning in the cancer clinic: an open-source, prototype framework for automatic contouring

John Asbach, Anurag Singh, Mark Farrugia, L. Shawn Matott, Anh Le

Author Affiliations +

Proceedings Volume 12037, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications; 120370F (2022) https://doi.org/10.1117/12.2611624
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

John Asbach, Anurag Singh, Mark Farrugia, L. Shawn Matott, and Anh Le "Accessible deep learning in the cancer clinic: an open-source, prototype framework for automatic contouring", Proc. SPIE 12037, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications, 120370F (4 April 2022); https://doi.org/10.1117/12.2611624

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available