Radiation therapy is one of the most widely used therapies for cancer treatment. A critical step in radiation therapy planning is to accurately delineate all organs at risk (OARs) to minimize potential adverse effects to healthy surrounding organs. However, manually delineating OARs based on computed tomography images is time-consuming and error-prone. Here, we present a deep learning model to automatically delineate OARs in head and neck, trained on a dataset of 215 computed tomography scans with 28 OARs manually delineated by experienced radiation oncologists. On a hold-out dataset of 100 computed tomography scans, our model achieves an average Dice similarity coefficient of 78.34% across the 28 OARs, significantly outperforming human experts and the previous state-of-the-art method by 10.05% and 5.18%, respectively. Our model takes only a few seconds to delineate an entire scan, compared to over half an hour by human experts. These findings demonstrate the potential for deep learning to improve the quality and reduce the treatment planning time of radiation therapy.
Mixed reality could revolutionize the way we learn about human body— Ronald van Loon (@Ronald_vanLoon) October 7, 2019
via @wef |
ArtificialIntelligence #AI #Healthcare #MachineLearning #ML #BigData #DataScientist #DataScience pic.twitter.com/18oX6816hq pic.twitter.com/Vy8tEVSGA2 pic.twitter.com/4GqSG4aNQJ https://t.co/u5lKDmJlkL pic.twitter.com/lGFj5usfXP