title: Multi-atlas learner fusion: An efficient segmentation approach for large-scale data.
abstract: 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.
authors.affiliation: Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Asman
authors.affiliation: Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA. Electronic address: yuankai.huo@vanderbilt.edu.
authors.forename: Yuankai
authors.initials: Y
authors.lastname: Huo
authors.affiliation: Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Plassard
authors.affiliation: Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Computer Science, Vanderbilt University, Nashville, TN 37235, USA; Institute of Imaging Science, Vanderbilt University, Nashville, TN 37235, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37235, USA.
authors.forename: Bennett A
authors.initials: BA
authors.lastname: Landman
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: AdaBoost; Machine learning; Multi-atlas learner fusion; Multi-atlas segmentation