Modeling Glioma Growth and Mass Effect in 3D MR Images of the Brain View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2007

AUTHORS

Cosmina Hogea , Christos Davatzikos , George Biros

ABSTRACT

In this article, we propose a framework for modeling glioma growth and the subsequent mechanical impact on the surrounding brain tissue (mass-effect) in a medical imaging context. Glioma growth is modeled via nonlinear reaction-advection-diffusion, with a two-way coupling with the underlying tissue elastic deformation. Tumor bulk and infiltration and subsequent mass-effects are not regarded separately, but captured by the model itself in the course of its evolution. Our formulation is fully Eulerian and naturally allows for updating the tumor diffusion coefficient following structural displacements caused by tumor growth/infiltration. We show that model parameters can be estimated via optimization based on imaging data, using efficient solution algorithms on regular grids. We test the model and the automatic optimization framework on real brain tumor data sets, achieving significant improvement in landmark prediction compared to a simplified purely mechanical approach. More... »

PAGES

642-650

References to SciGraph publications

  • 2000-11. Fractional step methods applied to a chemotaxis model in JOURNAL OF MATHEMATICAL BIOLOGY
  • 2005. Finite Element Modeling of Brain Tumor Mass-Effect from 3D Medical Images in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2005
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-540-75757-3_78

    DOI

    http://dx.doi.org/10.1007/978-3-540-75757-3_78

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1024475997

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/18051113


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    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"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Algorithms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Brain", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Brain Neoplasms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computer Simulation", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Glioma", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Image Enhancement", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Image Interpretation, Computer-Assisted", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Imaging, Three-Dimensional", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Magnetic Resonance Imaging", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Models, Biological", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Neoplasm Invasiveness", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Neoplasm Staging", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Reproducibility of Results", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Sensitivity and Specificity", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "name": [
                "Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia PA 19104, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hogea", 
            "givenName": "Cosmina", 
            "id": "sg:person.01311241617.43", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01311241617.43"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia PA 19104, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Davatzikos", 
            "givenName": "Christos", 
            "id": "sg:person.0746651452.04", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0746651452.04"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Departments of Mechanical Engineering and Applied Mechanics, Bioengineering, and Computer and Information Science, University of Pennsylvania, Philadelphia PA 19104, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Biros", 
            "givenName": "George", 
            "id": "sg:person.015577051337.44", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015577051337.44"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1117/12.237889", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007962013"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1046/j.1365-2184.2000.00177.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029104932"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11566465_50", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030322916", 
              "https://doi.org/10.1007/11566465_50"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11566465_50", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030322916", 
              "https://doi.org/10.1007/11566465_50"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s002850000038", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030335375", 
              "https://doi.org/10.1007/s002850000038"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0378-4371(03)00391-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042905280"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0378-4371(03)00391-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042905280"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmi.2005.857217", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061694777"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1142/s0218202599000300", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062964168"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/1.9780898718720", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1098555254"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2007", 
        "datePublishedReg": "2007-01-01", 
        "description": "In this article, we propose a framework for modeling glioma growth and the subsequent mechanical impact on the surrounding brain tissue (mass-effect) in a medical imaging context. Glioma growth is modeled via nonlinear reaction-advection-diffusion, with a two-way coupling with the underlying tissue elastic deformation. Tumor bulk and infiltration and subsequent mass-effects are not regarded separately, but captured by the model itself in the course of its evolution. Our formulation is fully Eulerian and naturally allows for updating the tumor diffusion coefficient following structural displacements caused by tumor growth/infiltration. We show that model parameters can be estimated via optimization based on imaging data, using efficient solution algorithms on regular grids. We test the model and the automatic optimization framework on real brain tumor data sets, achieving significant improvement in landmark prediction compared to a simplified purely mechanical approach.", 
        "editor": [
          {
            "familyName": "Ayache", 
            "givenName": "Nicholas", 
            "type": "Person"
          }, 
          {
            "familyName": "Ourselin", 
            "givenName": "S\u00e9bastien", 
            "type": "Person"
          }, 
          {
            "familyName": "Maeder", 
            "givenName": "Anthony", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-540-75757-3_78", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": {
          "isbn": [
            "978-3-540-75756-6"
          ], 
          "name": "Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2007", 
          "type": "Book"
        }, 
        "name": "Modeling Glioma Growth and Mass Effect in 3D MR Images of the Brain", 
        "pagination": "642-650", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-540-75757-3_78"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "749acb5a77cc4ff1d624fa8ee6c3dbfee8a1b45a7abf79ae46fe0e51238108ec"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1024475997"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "18051113"
            ]
          }
        ], 
        "publisher": {
          "location": "Berlin, Heidelberg", 
          "name": "Springer Berlin Heidelberg", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-540-75757-3_78", 
          "https://app.dimensions.ai/details/publication/pub.1024475997"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-16T05:46", 
        "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/v90/0000000347_0000000347/records_89812_00000001.jsonl", 
        "type": "Chapter", 
        "url": "https://link.springer.com/10.1007%2F978-3-540-75757-3_78"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    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.10.1007/978-3-540-75757-3_78'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-75757-3_78'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-75757-3_78'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-75757-3_78'


     

    This table displays all metadata directly associated to this object as RDF triples.

    181 TRIPLES      23 PREDICATES      51 URIs      36 LITERALS      24 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-540-75757-3_78 schema:about N10958bf724034ea5acfd73ac4c461452
    2 N1a9233206f024785903347c8cd112e9b
    3 N25aba0b8542447b1bc8c6493135ff27e
    4 N2e68a16e896c402b9b088dbb79d60732
    5 N3ea577aa5ac74274a564ad06844b3054
    6 N3f5a39c5d40e4366a68de9fad26618f3
    7 N42212612e0a542faabd874b0c3a9f003
    8 N5a2ecdde714c4fe0b5ebbed0604150e5
    9 N5fa7d8e088294f5f8e6e1bd60aaa021b
    10 N6e9c0d96443649908569886e37ecf719
    11 N701c1782758f4d218cc59bff3ace2bb1
    12 N97faae7a08074a3bbcd63b3a8bf0b46f
    13 N9b1fe443610e445694c2ad816e3c9878
    14 Na76eb016660440d4914eee77e8c00013
    15 Nd4e31cc7a774487d9cb791d554c7cb55
    16 anzsrc-for:08
    17 anzsrc-for:0801
    18 schema:author Nea4f7044c2bd458d937e7ae46d91b67e
    19 schema:citation sg:pub.10.1007/11566465_50
    20 sg:pub.10.1007/s002850000038
    21 https://doi.org/10.1016/s0378-4371(03)00391-1
    22 https://doi.org/10.1046/j.1365-2184.2000.00177.x
    23 https://doi.org/10.1109/tmi.2005.857217
    24 https://doi.org/10.1117/12.237889
    25 https://doi.org/10.1137/1.9780898718720
    26 https://doi.org/10.1142/s0218202599000300
    27 schema:datePublished 2007
    28 schema:datePublishedReg 2007-01-01
    29 schema:description In this article, we propose a framework for modeling glioma growth and the subsequent mechanical impact on the surrounding brain tissue (mass-effect) in a medical imaging context. Glioma growth is modeled via nonlinear reaction-advection-diffusion, with a two-way coupling with the underlying tissue elastic deformation. Tumor bulk and infiltration and subsequent mass-effects are not regarded separately, but captured by the model itself in the course of its evolution. Our formulation is fully Eulerian and naturally allows for updating the tumor diffusion coefficient following structural displacements caused by tumor growth/infiltration. We show that model parameters can be estimated via optimization based on imaging data, using efficient solution algorithms on regular grids. We test the model and the automatic optimization framework on real brain tumor data sets, achieving significant improvement in landmark prediction compared to a simplified purely mechanical approach.
    30 schema:editor N33d738ff21064a458c241976f3bb95fc
    31 schema:genre chapter
    32 schema:inLanguage en
    33 schema:isAccessibleForFree true
    34 schema:isPartOf N32697b449cc14510ae6ab1c8a28e0587
    35 schema:name Modeling Glioma Growth and Mass Effect in 3D MR Images of the Brain
    36 schema:pagination 642-650
    37 schema:productId N122f4264e3144d9382ee60d62716bb65
    38 N260b72bd0f9c413e98ef4ea61c05aedb
    39 N931e04bb0ec0462a8514b776f9e13e0f
    40 Nd0ad850181af4bbca5c54a02b0f39426
    41 schema:publisher Nb4a3f62e0c3c4a86a40048c1085253dc
    42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024475997
    43 https://doi.org/10.1007/978-3-540-75757-3_78
    44 schema:sdDatePublished 2019-04-16T05:46
    45 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    46 schema:sdPublisher Nf6136e49a9b44b93a6c2e3d551ca9b7e
    47 schema:url https://link.springer.com/10.1007%2F978-3-540-75757-3_78
    48 sgo:license sg:explorer/license/
    49 sgo:sdDataset chapters
    50 rdf:type schema:Chapter
    51 N0d10c4f46a3843bc8f6a080f03304d62 schema:familyName Ourselin
    52 schema:givenName Sébastien
    53 rdf:type schema:Person
    54 N10958bf724034ea5acfd73ac4c461452 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    55 schema:name Models, Biological
    56 rdf:type schema:DefinedTerm
    57 N122f4264e3144d9382ee60d62716bb65 schema:name doi
    58 schema:value 10.1007/978-3-540-75757-3_78
    59 rdf:type schema:PropertyValue
    60 N198935917054483c99b98a59a21db6ea schema:name Departments of Mechanical Engineering and Applied Mechanics, Bioengineering, and Computer and Information Science, University of Pennsylvania, Philadelphia PA 19104, USA
    61 rdf:type schema:Organization
    62 N1a9233206f024785903347c8cd112e9b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    63 schema:name Image Enhancement
    64 rdf:type schema:DefinedTerm
    65 N25aba0b8542447b1bc8c6493135ff27e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    66 schema:name Imaging, Three-Dimensional
    67 rdf:type schema:DefinedTerm
    68 N260b72bd0f9c413e98ef4ea61c05aedb schema:name readcube_id
    69 schema:value 749acb5a77cc4ff1d624fa8ee6c3dbfee8a1b45a7abf79ae46fe0e51238108ec
    70 rdf:type schema:PropertyValue
    71 N2e68a16e896c402b9b088dbb79d60732 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    72 schema:name Humans
    73 rdf:type schema:DefinedTerm
    74 N32697b449cc14510ae6ab1c8a28e0587 schema:isbn 978-3-540-75756-6
    75 schema:name Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
    76 rdf:type schema:Book
    77 N33d738ff21064a458c241976f3bb95fc rdf:first N7a2f127cc65e4189b676e196b6eb4fd3
    78 rdf:rest N3743e222d02b4fd0b9d420dd0fc701a3
    79 N3743e222d02b4fd0b9d420dd0fc701a3 rdf:first N0d10c4f46a3843bc8f6a080f03304d62
    80 rdf:rest N7193e27fe6a447298e1349b9e2b02953
    81 N3ea577aa5ac74274a564ad06844b3054 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    82 schema:name Image Interpretation, Computer-Assisted
    83 rdf:type schema:DefinedTerm
    84 N3f5a39c5d40e4366a68de9fad26618f3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    85 schema:name Glioma
    86 rdf:type schema:DefinedTerm
    87 N42212612e0a542faabd874b0c3a9f003 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    88 schema:name Reproducibility of Results
    89 rdf:type schema:DefinedTerm
    90 N5a2ecdde714c4fe0b5ebbed0604150e5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    91 schema:name Algorithms
    92 rdf:type schema:DefinedTerm
    93 N5fa7d8e088294f5f8e6e1bd60aaa021b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    94 schema:name Magnetic Resonance Imaging
    95 rdf:type schema:DefinedTerm
    96 N6c6863b7ff704108a78849deba877b62 schema:name Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia PA 19104, USA
    97 rdf:type schema:Organization
    98 N6e9c0d96443649908569886e37ecf719 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    99 schema:name Sensitivity and Specificity
    100 rdf:type schema:DefinedTerm
    101 N701c1782758f4d218cc59bff3ace2bb1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    102 schema:name Neoplasm Invasiveness
    103 rdf:type schema:DefinedTerm
    104 N7193e27fe6a447298e1349b9e2b02953 rdf:first N7a0522c84a034975ada4be5f6b3969f2
    105 rdf:rest rdf:nil
    106 N7967ee747c374bf4ae4b95850d915597 rdf:first sg:person.0746651452.04
    107 rdf:rest Ne19f0e79e93c44c0984091965075e142
    108 N7a0522c84a034975ada4be5f6b3969f2 schema:familyName Maeder
    109 schema:givenName Anthony
    110 rdf:type schema:Person
    111 N7a2f127cc65e4189b676e196b6eb4fd3 schema:familyName Ayache
    112 schema:givenName Nicholas
    113 rdf:type schema:Person
    114 N931e04bb0ec0462a8514b776f9e13e0f schema:name dimensions_id
    115 schema:value pub.1024475997
    116 rdf:type schema:PropertyValue
    117 N97faae7a08074a3bbcd63b3a8bf0b46f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    118 schema:name Computer Simulation
    119 rdf:type schema:DefinedTerm
    120 N9b1fe443610e445694c2ad816e3c9878 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    121 schema:name Brain
    122 rdf:type schema:DefinedTerm
    123 Na76eb016660440d4914eee77e8c00013 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    124 schema:name Neoplasm Staging
    125 rdf:type schema:DefinedTerm
    126 Nb4a3f62e0c3c4a86a40048c1085253dc schema:location Berlin, Heidelberg
    127 schema:name Springer Berlin Heidelberg
    128 rdf:type schema:Organisation
    129 Nbdd5ac99a5c449388e773d74afd8519a schema:name Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia PA 19104, USA
    130 rdf:type schema:Organization
    131 Nd0ad850181af4bbca5c54a02b0f39426 schema:name pubmed_id
    132 schema:value 18051113
    133 rdf:type schema:PropertyValue
    134 Nd4e31cc7a774487d9cb791d554c7cb55 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    135 schema:name Brain Neoplasms
    136 rdf:type schema:DefinedTerm
    137 Ne19f0e79e93c44c0984091965075e142 rdf:first sg:person.015577051337.44
    138 rdf:rest rdf:nil
    139 Nea4f7044c2bd458d937e7ae46d91b67e rdf:first sg:person.01311241617.43
    140 rdf:rest N7967ee747c374bf4ae4b95850d915597
    141 Nf6136e49a9b44b93a6c2e3d551ca9b7e schema:name Springer Nature - SN SciGraph project
    142 rdf:type schema:Organization
    143 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    144 schema:name Information and Computing Sciences
    145 rdf:type schema:DefinedTerm
    146 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    147 schema:name Artificial Intelligence and Image Processing
    148 rdf:type schema:DefinedTerm
    149 sg:person.01311241617.43 schema:affiliation N6c6863b7ff704108a78849deba877b62
    150 schema:familyName Hogea
    151 schema:givenName Cosmina
    152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01311241617.43
    153 rdf:type schema:Person
    154 sg:person.015577051337.44 schema:affiliation N198935917054483c99b98a59a21db6ea
    155 schema:familyName Biros
    156 schema:givenName George
    157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015577051337.44
    158 rdf:type schema:Person
    159 sg:person.0746651452.04 schema:affiliation Nbdd5ac99a5c449388e773d74afd8519a
    160 schema:familyName Davatzikos
    161 schema:givenName Christos
    162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0746651452.04
    163 rdf:type schema:Person
    164 sg:pub.10.1007/11566465_50 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030322916
    165 https://doi.org/10.1007/11566465_50
    166 rdf:type schema:CreativeWork
    167 sg:pub.10.1007/s002850000038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030335375
    168 https://doi.org/10.1007/s002850000038
    169 rdf:type schema:CreativeWork
    170 https://doi.org/10.1016/s0378-4371(03)00391-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042905280
    171 rdf:type schema:CreativeWork
    172 https://doi.org/10.1046/j.1365-2184.2000.00177.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1029104932
    173 rdf:type schema:CreativeWork
    174 https://doi.org/10.1109/tmi.2005.857217 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061694777
    175 rdf:type schema:CreativeWork
    176 https://doi.org/10.1117/12.237889 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007962013
    177 rdf:type schema:CreativeWork
    178 https://doi.org/10.1137/1.9780898718720 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098555254
    179 rdf:type schema:CreativeWork
    180 https://doi.org/10.1142/s0218202599000300 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062964168
    181 rdf:type schema:CreativeWork
     




    Preview window. Press ESC to close (or click here)


    ...