A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
---|---|
Title of host publication | Information Processing in Medical Imaging : 26th International Conference, IPMI 2019, Proceedings |
Editors | Siqi Bao, Albert C.S. Chung, James C. Gee, Paul A. Yushkevich |
Publisher | Springer |
Publication date | 1 Jan 2019 |
Pages | 360-371 |
ISBN (Print) | 9783030203504 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Event | 26th International Conference on Information Processing in Medical Imaging, IPMI 2019 - Hong Kong, China Duration: 2 Jun 2019 → 7 Jun 2019 |
Conference
Conference | 26th International Conference on Information Processing in Medical Imaging, IPMI 2019 |
---|---|
Land | China |
By | Hong Kong |
Periode | 02/06/2019 → 07/06/2019 |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|
Volume | 11492 LNCS |
ISSN | 0302-9743 |
- Bayesian transfer learning, Image segmentation, Variational inference
Research areas
Links
- http://arxiv.org/pdf/1903.04191
Submitted manuscript
ID: 226392835