Atlas Encoding by Randomized Forests for Efficient Label Propagation
- 1 January 2013
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC in Lecture Notes in Computer Science
- Vol. 16, 66-73
- https://doi.org/10.1007/978-3-642-40760-4_9
Abstract
We propose a method for multi-atlas label propagation based on encoding the individual atlases by randomized classification forests. Most current approaches perform a non-linear registration between all atlases and the target image, followed by a sophisticated fusion scheme. While these approaches can achieve high accuracy, in general they do so at high computational cost. This negatively affects the scalability to large databases and experimentation. To tackle this issue, we propose to use a small and deep classification forest to encode each atlas individually in reference to an aligned probabilistic atlas, resulting in an Atlas Forest (AF). At test time, each AF yields a probabilistic label estimate, and fusion is done by averaging. Our scheme performs only one registration per target image, achieves good results with a simple fusion scheme, and allows for efficient experimentation. In contrast to standard forest schemes, incorporation of new scans is possible without retraining, and target-specific selection of atlases remains possible. The evaluation on three different databases shows accuracy at the level of the state of the art, at a significantly lower runtime.Keywords
This publication has 16 references indexed in Scilit:
- Spatially Aware Patch-Based Segmentation (SAPS): An Alternative Patch-Based Segmentation FrameworkLecture Notes in Computer Science, 2013
- Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MRLecture Notes in Computer Science, 2012
- Combining Generative and Discriminative Models for Semantic Segmentation of CT Scans via Active LearningLecture Notes in Computer Science, 2011
- Entangled Decision Forests and Their Application for Semantic Segmentation of CT ImagesLecture Notes in Computer Science, 2011
- Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentationNeuroImage, 2011
- Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracyNeuroImage, 2009
- Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registrationNeuroImage, 2009
- Construction of a 3D probabilistic atlas of human cortical structuresNeuroImage, 2007
- Automatic anatomical brain MRI segmentation combining label propagation and decision fusionNeuroImage, 2006
- Unbiased diffeomorphic atlas construction for computational anatomyNeuroImage, 2004