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

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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