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      An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts

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          Abstract

          We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.

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          Most cited references28

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          Hierarchical clustering schemes.

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            A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics.

            Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity. Subsequently, we apply this improved covariance estimator (which has guaranteed minimum mean squared error, is well-conditioned, and is always positive definite even for small sample sizes) to the problem of inferring large-scale gene association networks. We show that it performs very favorably compared to competing approaches both in simulations as well as in application to real expression data.
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              Spectral grouping using the Nyström method.

              Spectral graph theoretic methods have recently shown great promise for the problem of image segmentation. However, due to the computational demands of these approaches, applications to large problems such as spatiotemporal data and high resolution imagery have been slow to appear. The contribution of this paper is a method that substantially reduces the computational requirements of grouping algorithms based on spectral partitioning making it feasible to apply them to very large grouping problems. Our approach is based on a technique for the numerical solution of eigenfunction problems known as the Nyström method. This method allows one to extrapolate the complete grouping solution using only a small number of samples. In doing so, we leverage the fact that there are far fewer coherent groups in a scene than pixels.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2015
                30 July 2015
                : 10
                : 7
                : e0133337
                Affiliations
                [1 ]Department of Biomedical Engineering, Korea University, Seoul, Republic of Korea
                [2 ]Department of Computer Science, KAIST, Daejeon, Republic of Korea
                [3 ]I 2BM, CEA, Gif-sur-Yvette, France
                [4 ]Institut Fédératif de Recherche 49, Gif-sur-Yvette, France
                [5 ]University of Concepción, Concepción, Chile
                [6 ]Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
                [7 ]Handong Global University, Pohang, Republic of Korea
                Istituto Italiano di Tecnologia, ITALY
                Author notes

                Competing Interests: The authors confirm that the affiliation to Samsung of the first author (Sang Wook Yoo) does not alter their adherence to PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: SWY JKS JSS. Performed the experiments: SWY PG. Analyzed the data: SWY PG YJ KY. Contributed reagents/materials/analysis tools: JKS JFM. Wrote the paper: SWY JKS JSS.

                [¤]

                R&D Team, Health and Medical Equipment Business, Samsung Electronics, Suwon, Republic of Korea

                Article
                PONE-D-14-31339
                10.1371/journal.pone.0133337
                4520495
                26225419
                51ffbbb1-547b-4791-8f8d-0d6b0a554d9c
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 29 July 2014
                : 25 June 2015
                Page count
                Figures: 12, Tables: 3, Pages: 24
                Funding
                This work was partially supported by the National Research Foundation of Korea (NRF) (No. NRF-2012R1A1B3004157), a grant of the Korea 565 Health Technology R&D Project through the Korea Health Industry Development 566 Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea 567 (grant number: HI14C2768), and by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government (MSIP) (No. B0101-15-247). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All relevant data are available from Figshare: http://dx.doi.org/10.6084/m9.figshare.1476988.

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