A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.

OriginalsprogEngelsk
TitelInformation Processing in Medical Imaging : 26th International Conference, IPMI 2019, Proceedings
RedaktørerSiqi Bao, Albert C.S. Chung, James C. Gee, Paul A. Yushkevich
ForlagSpringer
Publikationsdato1 jan. 2019
Sider360-371
ISBN (Trykt)9783030203504
DOI
StatusUdgivet - 1 jan. 2019
Begivenhed26th International Conference on Information Processing in Medical Imaging, IPMI 2019 - Hong Kong, Kina
Varighed: 2 jun. 20197 jun. 2019

Konference

Konference26th International Conference on Information Processing in Medical Imaging, IPMI 2019
LandKina
ByHong Kong
Periode02/06/201907/06/2019
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind11492 LNCS
ISSN0302-9743

ID: 226392835