Automated detection and quantification of residual brain tumor using an interactive computer-aided detection scheme

Kevin P. Gaffney; Faranak Aghaei; James Battiste; Bin Zheng

doi:10.1117/12.2254501

3 March 2017 Automated detection and quantification of residual brain tumor using an interactive computer-aided detection scheme

Kevin P. Gaffney, Faranak Aghaei, James Battiste, Bin Zheng

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101342I (2017) https://doi.org/10.1117/12.2254501
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Detection of residual brain tumor is important to evaluate efficacy of brain cancer surgery, determine optimal strategy of further radiation therapy if needed, and assess ultimate prognosis of the patients. Brain MR is a commonly used imaging modality for this task. In order to distinguish between residual tumor and surgery induced scar tissues, two sets of MRI scans are conducted pre- and post-gadolinium contrast injection. The residual tumors are only enhanced in the post-contrast injection images. However, subjective reading and quantifying this type of brain MR images faces difficulty in detecting real residual tumor regions and measuring total volume of the residual tumor. In order to help solve this clinical difficulty, we developed and tested a new interactive computer-aided detection scheme, which consists of three consecutive image processing steps namely, 1) segmentation of the intracranial region, 2) image registration and subtraction, 3) tumor segmentation and refinement. The scheme also includes a specially designed and implemented graphical user interface (GUI) platform. When using this scheme, two sets of pre- and post-contrast injection images are first automatically processed to detect and quantify residual tumor volume. Then, a user can visually examine segmentation results and conveniently guide the scheme to correct any detection or segmentation errors if needed. The scheme has been repeatedly tested using five cases. Due to the observed high performance and robustness of the testing results, the scheme is currently ready for conducting clinical studies and helping clinicians investigate the association between this quantitative image marker and outcome of patients.

Citation Download Citation

Kevin P. Gaffney, Faranak Aghaei, James Battiste, and Bin Zheng "Automated detection and quantification of residual brain tumor using an interactive computer-aided detection scheme", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342I (3 March 2017); https://doi.org/10.1117/12.2254501

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Tumors

Brain

Magnetic resonance imaging

Surgery

Image processing

Neuroimaging

Show All Keywords

Keywords/Phrases

Search In:

Publication Years