Organ-at-Risk (OAR) segmentation in head and neck CT using U-RCNN

Yang Lei; Joseph Harms; Xue Dong; Tonghe Wang; Xiangyang Tang; David S. Yu; Jonathan J. Beitler; Walter J. Curran; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2549782

16 March 2020 Organ-at-Risk (OAR) segmentation in head and neck CT using U-RCNN

Yang Lei, Joseph Harms, Xue Dong, Tonghe Wang, Xiangyang Tang, David S. Yu, Jonathan J. Beitler, Walter J. Curran, Tian Liu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 1131444 (2020) https://doi.org/10.1117/12.2549782
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Radiation treatment for head-and-neck (HN) cancers requires accurate treatment planning based on 3D patient models derived from CT images. In clinical practice, the treatment volumes and organs-at-risk (OARs) are manually contoured by experienced physicians. This tedious and time-consuming procedure limits clinical workflow and resources. In this work, we propose to use a 3D Faster R-CNN to automatically detect the location of head and neck organs, then apply a U-Net to segment the multi-organ contours, called U-RCNN. The mean Dice similarity coefficient (DSC) of esophagus, larynx, mandible, oral cavity, left parotid, right parotid, pharynx and spinal cord were ranging from 79% to 89%, which demonstrated the segmentation accuracy of the proposed U-RCNN method. This segmentation technique could be a useful tool to facilitate routine clinical workflow in H&N radiotherapy.

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Yang Lei, Joseph Harms, Xue Dong, Tonghe Wang, Xiangyang Tang, David S. Yu, Jonathan J. Beitler, Walter J. Curran, Tian Liu, and Xiaofeng Yang "Organ-at-Risk (OAR) segmentation in head and neck CT using U-RCNN", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131444 (16 March 2020); https://doi.org/10.1117/12.2549782

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