Multi-atlas learner fusion: An efficient segmentation approach for large-scale data.

Asman AJ, Huo Y, Plassard AJ, Landman BA
Med Image Anal. 2015 26 (1): 82-91

PMID: 26363845 · PMCID: PMC4679591 · DOI:10.1016/j.media.2015.08.010

We propose multi-atlas learner fusion (MLF), a framework for rapidly and accurately replicating the highly accurate, yet computationally expensive, multi-atlas segmentation framework based on fusing local learners. In the largest whole-brain multi-atlas study yet reported, multi-atlas segmentations are estimated for a training set of 3464 MR brain images. Using these multi-atlas estimates we (1) estimate a low-dimensional representation for selecting locally appropriate example images, and (2) build AdaBoost learners that map a weak initial segmentation to the multi-atlas segmentation result. Thus, to segment a new target image we project the image into the low-dimensional space, construct a weak initial segmentation, and fuse the trained, locally selected, learners. The MLF framework cuts the runtime on a modern computer from 36 h down to 3-8 min - a 270× speedup - by completely bypassing the need for deformable atlas-target registrations. Additionally, we (1) describe a technique for optimizing the weak initial segmentation and the AdaBoost learning parameters, (2) quantify the ability to replicate the multi-atlas result with mean accuracies approaching the multi-atlas intra-subject reproducibility on a testing set of 380 images, (3) demonstrate significant increases in the reproducibility of intra-subject segmentations when compared to a state-of-the-art multi-atlas framework on a separate reproducibility dataset, (4) show that under the MLF framework the large-scale data model significantly improve the segmentation over the small-scale model under the MLF framework, and (5) indicate that the MLF framework has comparable performance as state-of-the-art multi-atlas segmentation algorithms without using non-local information.

Copyright © 2015 Elsevier B.V. All rights reserved.

MeSH Terms (14)

Algorithms Brain Connectome Databases, Factual Humans Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Magnetic Resonance Imaging Multimodal Imaging Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Subtraction Technique

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