Estimation of Registration Accuracy Applied to Multi-Atlas Segmentation
R. Datteri, A. Asman, B. Landman, and B. Dawant. “Estimation of Registration Accuracy Applied to Multi-Atlas Segmentation.” In . Toronto, Canada, September 2011 (Oral Presentation)
Full text: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.728.4212&rep=rep1&type=pdf
Abstract
Multi-atlas registration-based segmentation has recently become a
popular technique in medical imaging. Since the quality of individual atlas
segmentations affect the quality of the results, atlas selection and atlas fusion
have become important areas of research for multi-atlas segmentation. In this
paper, we present an automatic technique that approximately calculates the
quality of registration. We applied our method to multi-atlas segmentation and
find that our measure correlates strongly ( = 0.79) with the ground truth
DICE similarity index. When applied to atlas fusion using a majority vote
technique weighted by our measure of registration quality, our algorithm
performs statistically better than both an un-weighted majority vote technique
and a voting technique weighted by residual normalized mutual information.
