TY - GEN
T1 - Organ-at-Risk (OAR) segmentation in head and neck CT using U-RCNN
AU - Lei, Yang
AU - Harms, Joseph
AU - Dong, Xue
AU - Wang, Tonghe
AU - Tang, Xiangyang
AU - Yu, David S.
AU - Beitler, Jonathan J.
AU - Curran, Walter J.
AU - Liu, Tian
AU - Yang, Xiaofeng
N1 - Publisher Copyright:
© 2020 SPIE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85085509608
U2 - 10.1117/12.2549782
DO - 10.1117/12.2549782
M3 - Conference contribution
AN - SCOPUS:85085509608
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2020
A2 - Hahn, Horst K.
A2 - Mazurowski, Maciej A.
PB - SPIE
T2 - Medical Imaging 2020: Computer-Aided Diagnosis
Y2 - 16 February 2020 through 19 February 2020
ER -