A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Classifier-Based Multi-atlas Label Propagation with Test-Specific Atlas Weighting for Correspondence-Free Scenarios
[chapter]
2014
Lecture Notes in Computer Science
We propose a segmentation method which transfers the advantages of multi-atlas label propagation (MALP) to correspondence-free scenarios. MALP is a branch of segmentation approaches with attractive properties, which is currently applicable only in correspondence-based regimes such as brain labeling, which assume correspondence between atlases and test image. This precludes its use for the large class of tasks without this property, such as tumor segmentation. In this work, we propose a method
doi:10.1007/978-3-319-13972-2_11
fatcat:rlv7poczdng4vgyps3h7qu6voa