Deep similarity learning using a Siamese ResNet trained on similarity labels from disparity maps of cerebral MRA MIP pairs

Christian Neumann; Klaus D. Tönnies; Regina Pohle-Fröhlich

doi:10.1117/12.2548576

10 March 2020 Deep similarity learning using a Siamese ResNet trained on similarity labels from disparity maps of cerebral MRA MIP pairs

Christian Neumann, Klaus D. Tönnies, Regina Pohle-Fröhlich

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113132N (2020) https://doi.org/10.1117/12.2548576
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Similarity measures are a critical component for iterative registration tasks. In intrinsic 2D/3D registration an initialization close to the solution is usually needed for convergence. Traditional similarity measures are limited in their applicability to perspective projection data. Deep Learning allows us to create similarity measures that encode almost arbitrary non-linear relationships like perspective projection. We apply a siamese network and a 2-channel network to the problem of comparing two perspective maximum intensity projections of medical data. We propose the use of residual units as the building block. A major challenge is to define similarity under perspective projection so that it can be used for training. Extrinsic registration gives us a gold standard of registered examples but we need a way to quantify the dissimilarity of unregistered image pairs for training. We propose the use of disparity as the basis for construction of similarity labels, i.e. labels are based on distances between projected points when viewing the same 3D point through different cameras. The final similarity labels are independent of the choice of camera parametrization and describe the visual similarity of two given views of a common 3D model. We compare our deep similarity metric to traditional similarity measures for a comparative evaluation. Our best configuration has a Pearson correlation coefficient of 0.792 and a Spearman’s rank correlation coefficient of 0.480. The best traditional method is normalized cross correlation with a 5.4% lower Spearman’s rank correlation coefficient.

Citation Download Citation

Christian Neumann, Klaus D. Tönnies, and Regina Pohle-Fröhlich "Deep similarity learning using a Siamese ResNet trained on similarity labels from disparity maps of cerebral MRA MIP pairs", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132N (10 March 2020); https://doi.org/10.1117/12.2548576

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
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Cameras

Image registration

Distance measurement

Network architectures

Magnetic resonance imaging

Image processing

Machine learning

Show All Keywords

Keywords/Phrases

Search In:

Publication Years