title: Encoding atlases by randomized classification forests for efficient multi-atlas label propagation.
abstract: We propose a method for multi-atlas label propagation (MALP) 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 might negatively affect 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). Our classifier-based encoding differs from current MALP approaches, which represent each point in the atlas either directly as a single image/label value pair, or by a set of corresponding patches. At test time, each AF produces one probabilistic label estimate, and their 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, in which each tree would be trained on all atlases, our approach retains the advantages of the standard MALP framework. The target-specific selection of atlases remains possible, and incorporation of new scans is straightforward without retraining. The evaluation on four different databases shows accuracy within the range of the state of the art at a significantly lower running time.
authors.affiliation: Microsoft Research, 21 Station Road, Cambridge CB1 2FB, United Kingdom. Electronic address: darko.zikic@tum.de.
authors.forename: D
authors.initials: D
authors.lastname: Zikic
authors.affiliation: Biomedical Image Analysis Group, Imperial College London, 180 Queen's Gate, London SW7 2AZ, United Kingdom.
authors.forename: B
authors.initials: B
authors.lastname: Glocker
authors.affiliation: Microsoft Research, 21 Station Road, Cambridge CB1 2FB, United Kingdom.
authors.forename: A
authors.initials: A
authors.lastname: Criminisi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004282:Documentation / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: Brain; Multi-atlas label propagation; Randomized forest; Segmentation