Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at https://www.ucl.ac.uk/finance/research/rs-contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
Measuring brain atrophy with a generalized formulation of the boundary shift integral.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Comparative Study
  • Authors:
    Prados F, Cardoso MJ, Leung KK, Cash DM, Modat M, Fox NC, Wheeler-Kingshott CAM, Ourselin S, Alzheimer's Disease Neuroimaging Initiative
  • Publication date:
  • Pagination:
    S81, S90
  • Journal:
    Neurobiol Aging
  • Volume:
    36 Suppl 1
  • Status:
  • Country:
    United States
  • PII:
  • Language:
  • Keywords:
    Alzheimer's disease, Biomarker, Clinical trials, MRI, boundary shift integral, Aged, Aged, 80 and over, Alzheimer Disease, Atrophy, Brain, Diffusion Magnetic Resonance Imaging, Female, Humans, Male, Middle Aged, Neuroimaging
Brain atrophy measured using structural magnetic resonance imaging (MRI) has been widely used as an imaging biomarker for disease diagnosis and tracking of pathologic progression in neurodegenerative diseases. In this work, we present a generalized and extended formulation of the boundary shift integral (gBSI) using probabilistic segmentations to estimate anatomic changes between 2 time points. This method adaptively estimates a non-binary exclusive OR region of interest from probabilistic brain segmentations of the baseline and repeat scans to better localize and capture the brain atrophy. We evaluate the proposed method by comparing the sample size requirements for a hypothetical clinical trial of Alzheimer's disease to that needed for the current implementation of BSI as well as a fuzzy implementation of BSI. The gBSI method results in a modest but reduced sample size, providing increased sensitivity to disease changes through the use of the probabilistic exclusive OR region.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers Show More
Neurodegenerative Diseases
Neurodegenerative Diseases
Dept of Med Phys & Biomedical Eng
Dept of Med Phys & Biomedical Eng
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by