Ontology type: schema:Chapter
2015
AUTHORSAndre Reichenbach , Mathias Goldau , Christian Heine , Mario Hlawitschka
ABSTRACTFiber clustering algorithms are employed to find patterns in the structural connections of the human brain as traced by tractography algorithms. Current clustering algorithms often require the calculation of large similarity matrices and thus do not scale well for datasets beyond 100,000 streamlines. We extended and adapted the 2D vector field k–means algorithm of Ferreira et al. to find bundles in 3D tractography data from diffusion MRI (dMRI) data. The resulting algorithm is linear in the number of line segments in the fiber data and can cluster large datasets without the use of random sampling or complex multipass procedures. It copes with interrupted streamlines and allows multisubject comparisons. More... »
PAGES191-198
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
ISBN
978-3-319-24552-2
978-3-319-24553-9
http://scigraph.springernature.com/pub.10.1007/978-3-319-24553-9_24
DOIhttp://dx.doi.org/10.1007/978-3-319-24553-9_24
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1009270357
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Leipzig University",
"id": "https://www.grid.ac/institutes/grid.9647.c",
"name": [
"Image and Signal Processing Group, Computer Sience Institute, Leipzig University"
],
"type": "Organization"
},
"familyName": "Reichenbach",
"givenName": "Andre",
"id": "sg:person.011556526552.85",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011556526552.85"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Leipzig University",
"id": "https://www.grid.ac/institutes/grid.9647.c",
"name": [
"Image and Signal Processing Group, Computer Sience Institute, Leipzig University"
],
"type": "Organization"
},
"familyName": "Goldau",
"givenName": "Mathias",
"id": "sg:person.013306566756.54",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013306566756.54"
],
"type": "Person"
},
{
"affiliation": {
"name": [
"Visual Computing Group, Department of Computer Science, TU Chemnitz"
],
"type": "Organization"
},
"familyName": "Heine",
"givenName": "Christian",
"id": "sg:person.01322065347.76",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322065347.76"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Leipzig University",
"id": "https://www.grid.ac/institutes/grid.9647.c",
"name": [
"Scientific Visualization Group, Computer Sience Institute, Leipzig University"
],
"type": "Organization"
},
"familyName": "Hlawitschka",
"givenName": "Mario",
"id": "sg:person.01217101266.94",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01217101266.94"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1111/cgf.12107",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004202284"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2013.04.066",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004908877"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2010.07.038",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012040588"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3389/fnins.2012.00175",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016640366"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-15705-9_67",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028730107",
"https://doi.org/10.1007/978-3-642-15705-9_67"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-15705-9_67",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028730107",
"https://doi.org/10.1007/978-3-642-15705-9_67"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2010.01.004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029586519"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neuroimage.2010.07.050",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042426066"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3389/fninf.2014.00087",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049549105"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tvcg.2008.52",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061813077"
],
"type": "CreativeWork"
}
],
"datePublished": "2015",
"datePublishedReg": "2015-01-01",
"description": "Fiber clustering algorithms are employed to find patterns in the structural connections of the human brain as traced by tractography algorithms. Current clustering algorithms often require the calculation of large similarity matrices and thus do not scale well for datasets beyond 100,000 streamlines. We extended and adapted the 2D vector field k\u2013means algorithm of Ferreira et al. to find bundles in 3D tractography data from diffusion MRI (dMRI) data. The resulting algorithm is linear in the number of line segments in the fiber data and can cluster large datasets without the use of random sampling or complex multipass procedures. It copes with interrupted streamlines and allows multisubject comparisons.",
"editor": [
{
"familyName": "Navab",
"givenName": "Nassir",
"type": "Person"
},
{
"familyName": "Hornegger",
"givenName": "Joachim",
"type": "Person"
},
{
"familyName": "Wells",
"givenName": "William M.",
"type": "Person"
},
{
"familyName": "Frangi",
"givenName": "Alejandro",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-319-24553-9_24",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-319-24552-2",
"978-3-319-24553-9"
],
"name": "Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015",
"type": "Book"
},
"name": "V\u2013Bundles: Clustering Fiber Trajectories from Diffusion MRI in Linear Time",
"pagination": "191-198",
"productId": [
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-319-24553-9_24"
]
},
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"2c291af7b5943c197acdcd8f5aa0f3d6b47c262a55dea77368a41e9e45446e72"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1009270357"
]
}
],
"publisher": {
"location": "Cham",
"name": "Springer International Publishing",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-319-24553-9_24",
"https://app.dimensions.ai/details/publication/pub.1009270357"
],
"sdDataset": "chapters",
"sdDatePublished": "2019-04-15T15:19",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8672_00000248.jsonl",
"type": "Chapter",
"url": "http://link.springer.com/10.1007/978-3-319-24553-9_24"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-24553-9_24'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-24553-9_24'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-24553-9_24'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-24553-9_24'
This table displays all metadata directly associated to this object as RDF triples.
132 TRIPLES
23 PREDICATES
36 URIs
20 LITERALS
8 BLANK NODES