Brain Extraction Using Label Propagation and Group Agreement: Pincram
Publication Date
July 10, 2015
Journal
PLOS ONE
Authors
Rolf A. Heckemann, Christian Ledig, Katherine R. Gray, Paul Aljabar, et al
Volume
10
Issue
7
Pages
e0129211
DOI
http://doi.org/10.1371/journal.pone.0129211
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0129211
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26161961
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4498771
Europe PMC
http://europepmc.org/abstract/MED/26161961
Web of Science
000358162300008
Scopus
84940562220
Mendeley
http://www.mendeley.com/research/brain-extraction-using-label-propagation-group-agreement-pincram
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2170253"], "description"=>"<p>Images were visually centred at the level of the commissures approximately in the centre of the left thalamus to acquire a transverse (top rows) and a sagittal (bottom rows) slice. Left column, <i>O, H, L</i>: manual reference masks, <i>X</i>: generated mask (<i>OX</i> setup). Middle column, <i>O, H, L, X</i>: generated masks (<i>HX</i> setup in the case of X). Right column, <i>O, H, L, X</i>: discrepancies between the masks—green indicates overinclusion, red indicates underinclusion. Individual JCs were 0.9512 (<i>O</i>), 0.9704 (<i>H</i>), 0.9647 (<i>L</i>), and 0.9503 (<i>X</i>).</p>", "links"=>[], "tags"=>["image", "Segmentation Validation Engine", "methods exhibit disadvantages", "data", "iterative refinement approach", "consensus label", "Dice similarity coefficient", "mr"], "article_id"=>1479047, "categories"=>["Biological Sciences"], "users"=>["Rolf A. Heckemann", "Christian Ledig", "Katherine R. Gray", "Paul Aljabar", "Daniel Rueckert", "Joseph V. Hajnal", "Alexander Hammers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0129211.g001", "stats"=>{"downloads"=>1, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sample_images_chosen_randomly_from_each_dataset_/1479047", "title"=>"Sample images chosen randomly from each dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:16:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170254"], "description"=>"<p>Step numbers in the text correspond to numbered boxes. <i>C</i><sub><i>l</i></sub>: pre-selection fused mask; <i>M</i><sub><i>l</i></sub>: tight margin (boundary neighborhood) mask; <i>F</i><sub><i>l</i></sub>: fuzzy label summed from rank-selected subset; </p><p></p><p></p><p></p><p><mi>C</mi><mi>l</mi><mo>′</mo></p><p></p><p></p><p></p>: brain mask generated from <i>F</i><sub><i>l</i></sub> by thresholding and binarization; <p></p><p></p><p></p><p><mi>M</mi><mi>l</mi><mo>′</mo></p><p></p><p></p><p></p>: wide margin mask generated from <i>F</i><sub><i>l</i></sub> by thresholding and binarization<p></p>", "links"=>[], "tags"=>["image", "Segmentation Validation Engine", "methods exhibit disadvantages", "data", "iterative refinement approach", "consensus label", "Dice similarity coefficient", "mr"], "article_id"=>1479048, "categories"=>["Biological Sciences"], "users"=>["Rolf A. Heckemann", "Christian Ledig", "Katherine R. Gray", "Paul Aljabar", "Daniel Rueckert", "Joseph V. Hajnal", "Alexander Hammers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0129211.g002", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_diagram_of_pincram_workflow_/1479048", "title"=>"Overview diagram of pincram workflow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:16:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170255"], "description"=>"<p>Colours distinguish target data sets. Centre lines: median, boxes: interquartile range, whiskers: truncated range, dots: outliers, arrowheads: off-scale outliers.</p>", "links"=>[], "tags"=>["image", "Segmentation Validation Engine", "methods exhibit disadvantages", "data", "iterative refinement approach", "consensus label", "Dice similarity coefficient", "mr"], "article_id"=>1479049, "categories"=>["Biological Sciences"], "users"=>["Rolf A. Heckemann", "Christian Ledig", "Katherine R. Gray", "Paul Aljabar", "Daniel Rueckert", "Joseph V. Hajnal", "Alexander Hammers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0129211.g003", "stats"=>{"downloads"=>0, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Jaccard_coefficients_/1479049", "title"=>"Jaccard coefficients.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:16:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170256"], "description"=>"<p>Binary error maps images identifying false negative and false positive voxels were generated for all 40 individuals. After spatial normalization to a subject with a typical head shape (Subject 32) and averaging of the error maps, projection maps were created by summation along the cardinal axes. The maps are scaled individually to maximize the dynamic range. The procedure was adapted from <a href=\"http://sve.bmap.ucla.edu/instructions/metrics/projections/\" target=\"_blank\">http://sve.bmap.ucla.edu/instructions/metrics/projections/</a>. Top row: false positive, bottom row: false negative.</p>", "links"=>[], "tags"=>["image", "Segmentation Validation Engine", "methods exhibit disadvantages", "data", "iterative refinement approach", "consensus label", "Dice similarity coefficient", "mr"], "article_id"=>1479050, "categories"=>["Biological Sciences"], "users"=>["Rolf A. Heckemann", "Christian Ledig", "Katherine R. Gray", "Paul Aljabar", "Daniel Rueckert", "Joseph V. Hajnal", "Alexander Hammers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0129211.g004", "stats"=>{"downloads"=>5, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Projection_maps_for_LL_L_/1479050", "title"=>"Projection maps for <i>LL:L</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:16:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170257"], "description"=>"<p>Left: with overlay of reference segmentation. Centre: with overlay of failed generated segmentation (<i>HXO:O</i>) (one of four outliers in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129211#pone.0129211.g003\" target=\"_blank\">Fig 3</a>; JC 0.592, success index 0.884). Right: with overlay of successful segmentation using customized atlas (<i>HXOO:O</i>, JC 0.937, success index 0.977)</p>", "links"=>[], "tags"=>["image", "Segmentation Validation Engine", "methods exhibit disadvantages", "data", "iterative refinement approach", "consensus label", "Dice similarity coefficient", "mr"], "article_id"=>1479051, "categories"=>["Biological Sciences"], "users"=>["Rolf A. Heckemann", "Christian Ledig", "Katherine R. Gray", "Paul Aljabar", "Daniel Rueckert", "Joseph V. Hajnal", "Alexander Hammers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0129211.g005", "stats"=>{"downloads"=>1, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Subject_30_of_O_/1479051", "title"=>"Subject 30 of <b>O</b>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:16:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170258"], "description"=>"<p>X axis: <i>n</i><sub>0</sub> (ordinal scale), y axis: JC. Boxplot features as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129211#pone.0129211.g003\" target=\"_blank\">Fig 3</a>.</p>", "links"=>[], "tags"=>["image", "Segmentation Validation Engine", "methods exhibit disadvantages", "data", "iterative refinement approach", "consensus label", "Dice similarity coefficient", "mr"], "article_id"=>1479052, "categories"=>["Biological Sciences"], "users"=>["Rolf A. Heckemann", "Christian Ledig", "Katherine R. Gray", "Paul Aljabar", "Daniel Rueckert", "Joseph V. Hajnal", "Alexander Hammers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0129211.g006", "stats"=>{"downloads"=>1, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_atlases_used_versus_accuracy_achieved_/1479052", "title"=>"Number of atlases used versus accuracy achieved.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:16:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170259"], "description"=>"<p>X axes show the success index (JC between </p><p></p><p></p><p></p><p><mi>C</mi><mn>2</mn><mo>′</mo></p><p></p><p></p><p></p> and <i>C</i><sub>2</sub>), and y axes show JC between the final generated mask <p></p><p></p><p></p><p><mi>C</mi><mn>2</mn><mo>′</mo></p><p></p><p></p><p></p> and the gold-standard reference label. Left: full range. Right: zoomed on non-outlier data (five outliers are off-scale). Experiments (corresponding to rows in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129211#pone.0129211.t003\" target=\"_blank\">Table 3</a>) are grouped by colour.<p></p>", "links"=>[], "tags"=>["image", "Segmentation Validation Engine", "methods exhibit disadvantages", "data", "iterative refinement approach", "consensus label", "Dice similarity coefficient", "mr"], "article_id"=>1479053, "categories"=>["Biological Sciences"], "users"=>["Rolf A. Heckemann", "Christian Ledig", "Katherine R. Gray", "Paul Aljabar", "Daniel Rueckert", "Joseph V. Hajnal", "Alexander Hammers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0129211.g007", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatterplot_showing_success_index_versus_true_accuracy_/1479053", "title"=>"Scatterplot showing success index versus true accuracy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:16:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170260"], "description"=>"<p>FS: field strength, diff.: differences</p><p>Overview of experiments.</p>", "links"=>[], "tags"=>["image", "Segmentation Validation Engine", "methods exhibit disadvantages", "data", "iterative refinement approach", "consensus label", "Dice similarity coefficient", "mr"], "article_id"=>1479054, "categories"=>["Biological Sciences"], "users"=>["Rolf A. Heckemann", "Christian Ledig", "Katherine R. Gray", "Paul Aljabar", "Daniel Rueckert", "Joseph V. Hajnal", "Alexander Hammers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0129211.t001", "stats"=>{"downloads"=>0, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_experiments_/1479054", "title"=>"Overview of experiments.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-10 03:16:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170261"], "description"=>"<p>Atl: atlases, Tgt: targets, Ref: Reference, CV: coefficient of variation, Gen: generated, Δ<i>V</i>: Volume error, |Δ<i>V</i>|: absolute volume error, SD: standard deviation, JC: Jaccard coefficient, Dice: Dice coefficient, SD95: symmetric surface distance (95th percentile). ± indicates standard deviation</p><p>Volumes and accuracy.</p>", "links"=>[], "tags"=>["image", "Segmentation Validation Engine", "methods exhibit disadvantages", "data", "iterative refinement approach", "consensus label", "Dice similarity coefficient", "mr"], "article_id"=>1479055, "categories"=>["Biological Sciences"], "users"=>["Rolf A. Heckemann", "Christian Ledig", "Katherine R. Gray", "Paul Aljabar", "Daniel Rueckert", "Joseph V. Hajnal", "Alexander Hammers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0129211.t002", "stats"=>{"downloads"=>1, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Volumes_and_accuracy_/1479055", "title"=>"Volumes and accuracy.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-10 03:16:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170262"], "description"=>"<p>Pearson’s r and p-value.</p><p>*: p-value below smallest representable number</p><p>Correlation of the success index with JC.</p>", "links"=>[], "tags"=>["image", "Segmentation Validation Engine", "methods exhibit disadvantages", "data", "iterative refinement approach", "consensus label", "Dice similarity coefficient", "mr"], "article_id"=>1479056, "categories"=>["Biological Sciences"], "users"=>["Rolf A. Heckemann", "Christian Ledig", "Katherine R. Gray", "Paul Aljabar", "Daniel Rueckert", "Joseph V. Hajnal", "Alexander Hammers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0129211.t003", "stats"=>{"downloads"=>2, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_of_the_success_index_with_JC_/1479056", "title"=>"Correlation of the success index with JC.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-10 03:16:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170263"], "description"=>"<div><p>Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisite for many neuroimaging methods. Most existing methods exhibit disadvantages in that they are laborious, yield inconsistent results, and/or require training data to closely match the data to be processed. Here, we present <i>pincram</i>, an automatic, versatile method for accurately labelling the adult brain on T1-weighted 3D MR head images. The method uses an iterative refinement approach to propagate labels from multiple atlases to a given target image using image registration. At each refinement level, a consensus label is generated. At the subsequent level, the search for the brain boundary is constrained to the neighbourhood of the boundary of this consensus label. The method achieves high accuracy (Jaccard coefficient > 0.95 on typical data, corresponding to a Dice similarity coefficient of > 0.97) and performs better than many state-of-the-art methods as evidenced by independent evaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, the program generates the \"success index,\" a scalar metadatum indicative of the accuracy of the output label. Pincram is available as open source software.</p></div>", "links"=>[], "tags"=>["image", "Segmentation Validation Engine", "methods exhibit disadvantages", "data", "iterative refinement approach", "consensus label", "Dice similarity coefficient", "mr"], "article_id"=>1479057, "categories"=>["Biological Sciences"], "users"=>["Rolf A. Heckemann", "Christian Ledig", "Katherine R. Gray", "Paul Aljabar", "Daniel Rueckert", "Joseph V. Hajnal", "Alexander Hammers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0129211", "stats"=>{"downloads"=>5, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Brain_Extraction_Using_Label_Propagation_and_Group_Agreement_Pincram_/1479057", "title"=>"Brain Extraction Using Label Propagation and Group Agreement: Pincram", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-10 03:16:13"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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