Automatic brain arteriovenous malformations segmentation on contrast CT images using combined region proposal network and V-Net

Yabo Fu; Yang Lei; Tonghe Wang; Xiaojun Jiang; Walter J. Curran; Tian Liu; Hui-kuo Shu; Xiaofeng Yang

doi:10.1117/12.2550385

16 March 2020 Automatic brain arteriovenous malformations segmentation on contrast CT images using combined region proposal network and V-Net

Yabo Fu, Yang Lei, Tonghe Wang, Xiaojun Jiang, Walter J. Curran, Tian Liu, Hui-kuo Shu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113142Y (2020) https://doi.org/10.1117/12.2550385
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Stereotactic radiosurgery (SRS) is widely used to obliterate arteriovenous malformations (AVMs). Its performance relies on the accuracy of delineating the target AVM. Manual segmentation during a framed SRS procedure is timeconsuming and subject to inter- and intra-observer variation. Therefore, it is important to develop an automatic segmentation method to delineate the AVM target from CT images. In this study, we retrospectively investigated 80 patients who were treated with SRS. Ground truth was manually generated by an experienced physician using both DSA and CT images. A fast region proposal network was first trained to propose a bounding box that contains the AVM lesion for detection. The bounding box was then used to guide image patch sampling process for V-Net training. In the testing stage, possible AVM locations were first proposed by the region proposal network. Subsequently, V-Net was used for the final label prediction. Both the region proposal network and V-Net were trained using 60 patients and tested using 20 patients. The mean Dice similarity coefficient (DSC) was calculated to evaluate the accuracy of the proposed method. The automatic contours were in very good agreement to the ground truth contours with an average DSC < 0.85.

Citation Download Citation

Yabo Fu, Yang Lei, Tonghe Wang, Xiaojun Jiang, Walter J. Curran, Tian Liu, Hui-kuo Shu, and Xiaofeng Yang "Automatic brain arteriovenous malformations segmentation on contrast CT images using combined region proposal network and V-Net", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142Y (16 March 2020); https://doi.org/10.1117/12.2550385

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