Multi-organ segmentation in head and neck MRI using U-Faster-RCNN

Yang Lei; Jun Zhou; Xue Dong; Tonghe Wang; Hui Mao; Mark McDonald; Walter J. Curran; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2549596

10 March 2020 Multi-organ segmentation in head and neck MRI using U-Faster-RCNN

Yang Lei, Jun Zhou, Xue Dong, Tonghe Wang, Hui Mao, Mark McDonald, Walter J. Curran, Tian Liu, Xiaofeng Yang

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113133A (2020) https://doi.org/10.1117/12.2549596
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Citation Download Citation

Yang Lei, Jun Zhou, Xue Dong, Tonghe Wang, Hui Mao, Mark McDonald, Walter J. Curran, Tian Liu, and Xiaofeng Yang "Multi-organ segmentation in head and neck MRI using U-Faster-RCNN", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113133A (10 March 2020); https://doi.org/10.1117/12.2549596

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