Deep attention mask regional convolutional neural network for head-and-neck MRI multi-organ auto-delineation

Xianjin Dai; Yang Lei; Tonghe Wang; Jun Zhou; Walter J. Curran; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2581131

15 February 2021 Deep attention mask regional convolutional neural network for head-and-neck MRI multi-organ auto-delineation

Xianjin Dai, Yang Lei, Tonghe Wang, Jun Zhou, Walter J. Curran, Tian Liu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115971B (2021) https://doi.org/10.1117/12.2581131
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

The superior soft-tissue contrast obtained by MRI allows contouring target and organs-at-risk (OARs) accurately and reliably in head-and-neck cancer radiotherapy. However, the delineating of OARs is labor-intensive and subjective. We propose a mask regional convolutional neural network (R-CNN) for fully automated OAR delineation in head-and-neck (HN) MRI. A deep attention feature pyramid network (DAFPN) was used as the backbone to extract pyramid features, while the location and size of each region-of-interest (ROI) are estimated by the feeding features extracted from DAFPN into a regional proposal network (RPN). The final OAR contours were predicted by feeding cropped features within these ROIs (location and size) into a subnetwork to perform segmentation within ROIs. A retrospective study was performed on 45 HN cancer patients who underwent radiation therapy. MR images and their manual contours were collected for training and testing the proposed method. Five-fold cross-validation method was conducted to evaluate the proposed method. The mean Dice similarity coefficients of brain stem, esophagus, larynx, mandible, optic chiasm, left optic nerve, right optic nerve, oral cavity, left parotid, right parotid, pharynx, and spinal cord are 0.88, 0.52, 0.88, 0.77, 0.56, 0.59, 0.57, 0.90, 0.81, 0.81, 0.76, and 0.69, respectively. After the model training, all OAR can be segmented within 10 seconds. The accurate HN OAR delineation enables further development of MRI-only based radiotherapy workflow for HN cancer.

Conference Presentation

Citation Download Citation

Xianjin Dai, Yang Lei, Tonghe Wang, Jun Zhou, Walter J. Curran, Tian Liu, and Xiaofeng Yang "Deep attention mask regional convolutional neural network for head-and-neck MRI multi-organ auto-delineation", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115971B (15 February 2021); https://doi.org/10.1117/12.2581131

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available