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Technical Note: Deep Learning approach for automatic detection and identification of patient positioning devices for radiation therapy
Author(s) -
Thomas David H.,
Schubert Leah K.,
Vinogradskiy Yevgeniy,
Nath Sameer,
Kavanagh Brian,
Miften Moyed,
Jones Bernard
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14338
Subject(s) - convolutional neural network , computer science , transfer of learning , artificial intelligence , deep learning , object detection , documentation , automation , identification (biology) , process (computing) , computer vision , machine learning , pattern recognition (psychology) , engineering , mechanical engineering , botany , biology , programming language , operating system
Purpose Automatic detection and identification of setup devices, using a deep convolutional neural network (CNN) for real‐time multiclass object detection, has the potential to reduce errors in the treatment delivery process by avoiding documentation errors. Methods A database of the setup device photos from the most recent 1200 patients treated at our institution was downloaded from the record and verify (R&V) system along with the corresponding setup notes. Images were manually labeled with bounding boxes of each device. A real‐time object detection CNN using the “you only look once” (YOLOv2) architecture was trained using transfer learning of a pretrained CNN (ResNet50). The CNN was trained to detect and identify 11 of the most common treatment accessories used at our institution. Results Using transfer learning of a CNN for multiclass object detection, we are able to automatically detect and identify setup devices in photographs with an accuracy of 96%. Conclusions Automation in radiation oncology has the potential to reduce risk. Automatic detection of setup devices is possible using a CNN and transfer learning. This work shows both the value of incident learning systems (ILS) in practice knowledge dissemination, and shows how automation of clinical processes and less reliance on manual documentation has the potential for risk reduction in radiation oncology treatments.