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Proceedings Paper

Multi-organ segmentation in head and neck MRI using U-Faster-RCNN
Author(s): Yang Lei; Jun Zhou; Xue Dong; Tonghe Wang; Hui Mao; Mark McDonald; Walter J. Curran; Tian Liu; Xiaofeng Yang
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Paper Abstract

Radiotherapy treatment is based on 3D anatomical models which require accurate organs-at-risk (OARs) delineation. In current clinical practice, the OARs are generally delineated from computed tomography (CT). Because of its superior soft-tissue contrast, magnetic resonance imaging (MRI) information can be introduced to improve the quality of these 3D OAR delineation and therefore the treatment plan itself. Manual segmentation of relevant tissue regions from MR image is a tedious and time-consuming procedure, which is also subject to inter- and intra-observer variation. In this work, we propose to use a 3D Faster R-CNN to automatically detect the locations of head and neck OARs, then utilize an attention U-Net to automatically segment the multiple OARs. We tested our method using 15 head and neck cancer patients. The mean Dice similarity coefficient (DSC) of esophagus, larynx, mandible, oral cavity, left parotid, right parotid, pharynx and spinal cord were 84%, 79%, 85%, 89%, 82%, 81%, 85% and 89%, which demonstrated the segmentation accuracy of the proposed U-Faster-RCNN method. This segmentation technique could be a useful tool to facilitate the routine clinical workflow of H&N radiotherapy.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113133A (10 March 2020); doi: 10.1117/12.2549596
Show Author Affiliations
Yang Lei, Emory Univ. (United States)
Jun Zhou, Emory Univ. (United States)
Xue Dong, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Hui Mao, Emory Univ. (United States)
Mark McDonald, 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. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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