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Proceedings Paper

Organ-at-Risk (OAR) segmentation in head and neck CT using U-RCNN
Author(s): Yang Lei; Joseph Harms; Xue Dong; Tonghe Wang; Xiangyang Tang; David S. Yu; Jonathan J. Beitler; Walter J. Curran; Tian Liu; Xiaofeng Yang
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Paper 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.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131444 (16 March 2020); doi: 10.1117/12.2549782
Show Author Affiliations
Yang Lei, Emory Univ. (United States)
Joseph Harms, Emory Univ. (United States)
Xue Dong, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Xiangyang Tang, Emory Univ. (United States)
David S. Yu, Emory Univ. (United States)
Jonathan J. Beitler, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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