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A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation
- Source :
- Lecture Notes in Computer Science ISBN: 9783030203504, INFORMATION PROCESSING IN MEDICAL IMAGING, IPMI 2019, 11492, 360-371
- Publication Year :
- 2019
- Publisher :
- Springer International Publishing, 2019.
-
Abstract
- 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.<br />Comment: 12 pages, 2 figures, 1 table. Accepted to the International Conference on Information Processing in Medical Imaging (2019)
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Bayesian probability
Computer Science - Computer Vision and Pattern Recognition
Machine Learning (stat.ML)
02 engineering and technology
Brain tissue
Bayesian inference
computer.software_genre
Machine Learning (cs.LG)
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Statistics - Machine Learning
Voxel
0202 electrical engineering, electronic engineering, information engineering
Segmentation
Training set
business.industry
Pattern recognition
020201 artificial intelligence & image processing
Artificial intelligence
business
Classifier (UML)
computer
Subjects
Details
- ISBN :
- 978-3-030-20350-4
- ISBNs :
- 9783030203504
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science ISBN: 9783030203504, INFORMATION PROCESSING IN MEDICAL IMAGING, IPMI 2019, 11492, 360-371
- Accession number :
- edsair.doi.dedup.....6643af5325d4a1a4fc8b02d727726940