Respiratory motion compensation for the robot-guided laser osteotome View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-03-03

AUTHORS

Alina Giger, Christoph Jud, Philippe C. Cattin

ABSTRACT

PurposeThe use of a robot-guided laser osteotome for median sternotomy is impeded by prohibiting cutting inaccuracies due to respiration-induced motions of the thorax. With this paper, we advance today’s methodologies in sternotomy procedures by introducing the concept of novel 3D functional cuts and a respiratory motion compensation algorithm for the computer-assisted and robot-guided laser osteotome, CARLO®.MethodsWe present a trajectory planning algorithm for performing 3D functional cuts at a constant cutting velocity. In addition, we propose the use of Gaussian process (GP) prediction in order to anticipate the sternum’s pose providing enough time for the CARLO® device to adjust the position of the laser source.ResultsWe analysed the performance of the proposed algorithms on a computer-based simulation framework of the CARLO® device. The median position error of the laser focal point has shown to be reduced from 0.22 mm without GP prediction to 0.19 mm with GP prediction.ConclusionThe encouraging simulation results support the proposed respiratory motion compensation algorithm for robot-guided laser osteotomy on the thorax. Successful compensation of the respiration-induced motion of the thorax opens doors for robot-guided laser sternotomy and the related novel cutting patterns. These functional cuts hold great potential to significantly improve postoperative sternal stability and therefore reduce pain and recovery time for the patient. By enabling functional cuts, we approach an important threshold moment in the history of osteotomy, creating innovative opportunities which reach far beyond the classic linear cutting patterns. More... »

PAGES

1751-1762

References to SciGraph publications

  • 2007. Prediction of Respiratory Motion with Wavelet-Based Multiscale Autoregression in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2007
  • 2009-05-07. Sternal plating for primary and secondary sternal closure; can it improve sternal stability? in JOURNAL OF CARDIOTHORACIC SURGERY
  • 2013. Respiratory Motion Compensation with Relevance Vector Machines in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 2009-06-04. Forecasting respiratory motion with accurate online support vector regression (SVRpred) in INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11548-017-1543-3

    DOI

    http://dx.doi.org/10.1007/s11548-017-1543-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1084031837

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28258401


    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/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Clinical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Algorithms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computer Simulation", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Imaging, Three-Dimensional", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Laser Therapy", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Motion", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Osteotomy", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Respiration", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Robotics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Sternotomy", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123, Allschwil, Switzerland", 
              "id": "http://www.grid.ac/institutes/grid.6612.3", 
              "name": [
                "Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123, Allschwil, Switzerland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Giger", 
            "givenName": "Alina", 
            "id": "sg:person.014020325017.05", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014020325017.05"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123, Allschwil, Switzerland", 
              "id": "http://www.grid.ac/institutes/grid.6612.3", 
              "name": [
                "Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123, Allschwil, Switzerland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Jud", 
            "givenName": "Christoph", 
            "id": "sg:person.010024631132.03", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010024631132.03"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123, Allschwil, Switzerland", 
              "id": "http://www.grid.ac/institutes/grid.6612.3", 
              "name": [
                "Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123, Allschwil, Switzerland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cattin", 
            "givenName": "Philippe C.", 
            "id": "sg:person.015501206005.19", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015501206005.19"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s11548-009-0355-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045434258", 
              "https://doi.org/10.1007/s11548-009-0355-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1749-8090-4-19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007581515", 
              "https://doi.org/10.1186/1749-8090-4-19"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-75759-7_81", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016500879", 
              "https://doi.org/10.1007/978-3-540-75759-7_81"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-40763-5_14", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006988604", 
              "https://doi.org/10.1007/978-3-642-40763-5_14"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-03-03", 
        "datePublishedReg": "2017-03-03", 
        "description": "PurposeThe use of a robot-guided laser osteotome for median sternotomy is impeded by prohibiting cutting inaccuracies due to respiration-induced motions of the thorax. With this paper, we advance today\u2019s methodologies in sternotomy procedures by introducing the concept of novel 3D functional cuts and a respiratory motion compensation algorithm for the computer-assisted and robot-guided laser osteotome, CARLO\u00ae.MethodsWe present a trajectory planning algorithm for performing 3D functional cuts at a constant cutting velocity. In addition, we propose the use of Gaussian process (GP) prediction in order to anticipate the sternum\u2019s pose providing enough time for the CARLO\u00ae device to adjust the position of the laser source.ResultsWe analysed the performance of the proposed algorithms on a computer-based simulation framework of the CARLO\u00ae device. The median position error of the laser focal point has shown to be reduced from 0.22\u00a0mm without GP prediction to 0.19\u00a0mm with GP prediction.ConclusionThe encouraging simulation results support the proposed respiratory motion compensation algorithm for robot-guided laser osteotomy on the thorax. Successful compensation of the respiration-induced motion of the thorax opens doors for robot-guided laser sternotomy and the related novel cutting patterns. These functional cuts hold great potential to significantly improve postoperative sternal stability and therefore reduce pain and recovery time for the patient. By enabling functional cuts, we approach an important threshold moment in the history of osteotomy, creating innovative opportunities which reach far beyond the classic linear cutting patterns.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s11548-017-1543-3", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1041191", 
            "issn": [
              "1861-6410", 
              "1861-6429"
            ], 
            "name": "International Journal of Computer Assisted Radiology and Surgery", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "10", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "12"
          }
        ], 
        "keywords": [
          "motion compensation algorithm", 
          "Gaussian process prediction", 
          "trajectory planning algorithm", 
          "GP predictions", 
          "median position error", 
          "planning algorithm", 
          "respiratory motion compensation", 
          "compensation algorithm", 
          "simulation framework", 
          "motion compensation", 
          "constant cutting velocity", 
          "algorithm", 
          "process prediction", 
          "simulation results", 
          "laser osteotome", 
          "position error", 
          "today's methodologies", 
          "respiration-induced motion", 
          "related novels", 
          "innovative opportunities", 
          "devices", 
          "methodology", 
          "prediction", 
          "framework", 
          "enough time", 
          "performance", 
          "error", 
          "great potential", 
          "concept", 
          "inaccuracy", 
          "Carlo", 
          "motion", 
          "time", 
          "use", 
          "focal point", 
          "door", 
          "order", 
          "opportunities", 
          "compensation", 
          "cutting velocity", 
          "point", 
          "laser focal point", 
          "patterns", 
          "results", 
          "novel", 
          "cut", 
          "laser osteotomy", 
          "position", 
          "laser source", 
          "source", 
          "threshold moment", 
          "successful compensation", 
          "procedure", 
          "recovery time", 
          "addition", 
          "osteotome", 
          "velocity", 
          "potential", 
          "moment", 
          "sternotomy procedures", 
          "stability", 
          "PurposeThe use", 
          "sternal stability", 
          "ResultsWe", 
          "thorax", 
          "history", 
          "osteotomy", 
          "MethodsWe", 
          "patients", 
          "sternum", 
          "median sternotomy", 
          "sternotomy", 
          "paper", 
          "pain"
        ], 
        "name": "Respiratory motion compensation for the robot-guided laser osteotome", 
        "pagination": "1751-1762", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1084031837"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11548-017-1543-3"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "28258401"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11548-017-1543-3", 
          "https://app.dimensions.ai/details/publication/pub.1084031837"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:37", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_752.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s11548-017-1543-3"
      }
    ]
     

    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/s11548-017-1543-3'

    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/s11548-017-1543-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11548-017-1543-3'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11548-017-1543-3'


     

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

    205 TRIPLES      21 PREDICATES      113 URIs      101 LITERALS      17 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11548-017-1543-3 schema:about N0fc77340f06e47eba95e55f1c8e51db1
    2 N10f3fe94662748a6baefae157bff3c0f
    3 N7d43c7b78aa24aaaae79808cf1af0e7f
    4 N8728c55c7c1f432ea56a6298583033e5
    5 N9ed194b24a2a4a30aca7332ca35c0eb6
    6 Na88a44a26af34d209900751746a40f52
    7 Nc1f00e19fc404614ac4de3191ff59bfe
    8 Nc779ae99d6574be9a78cbd1df5026aed
    9 Nc8a248a9bbb744878f1daf2e3f22b572
    10 Neb1ff8b91b5747e3a54b25cbb2ff3dff
    11 anzsrc-for:11
    12 anzsrc-for:1103
    13 schema:author Ne66514ab00094fffaadc3dc469235d28
    14 schema:citation sg:pub.10.1007/978-3-540-75759-7_81
    15 sg:pub.10.1007/978-3-642-40763-5_14
    16 sg:pub.10.1007/s11548-009-0355-5
    17 sg:pub.10.1186/1749-8090-4-19
    18 schema:datePublished 2017-03-03
    19 schema:datePublishedReg 2017-03-03
    20 schema:description PurposeThe use of a robot-guided laser osteotome for median sternotomy is impeded by prohibiting cutting inaccuracies due to respiration-induced motions of the thorax. With this paper, we advance today’s methodologies in sternotomy procedures by introducing the concept of novel 3D functional cuts and a respiratory motion compensation algorithm for the computer-assisted and robot-guided laser osteotome, CARLO®.MethodsWe present a trajectory planning algorithm for performing 3D functional cuts at a constant cutting velocity. In addition, we propose the use of Gaussian process (GP) prediction in order to anticipate the sternum’s pose providing enough time for the CARLO® device to adjust the position of the laser source.ResultsWe analysed the performance of the proposed algorithms on a computer-based simulation framework of the CARLO® device. The median position error of the laser focal point has shown to be reduced from 0.22 mm without GP prediction to 0.19 mm with GP prediction.ConclusionThe encouraging simulation results support the proposed respiratory motion compensation algorithm for robot-guided laser osteotomy on the thorax. Successful compensation of the respiration-induced motion of the thorax opens doors for robot-guided laser sternotomy and the related novel cutting patterns. These functional cuts hold great potential to significantly improve postoperative sternal stability and therefore reduce pain and recovery time for the patient. By enabling functional cuts, we approach an important threshold moment in the history of osteotomy, creating innovative opportunities which reach far beyond the classic linear cutting patterns.
    21 schema:genre article
    22 schema:isAccessibleForFree false
    23 schema:isPartOf N6e58a56b8d56418cb9fc72a758063de2
    24 Nb9cc6797177444d780ef14fa76356a64
    25 sg:journal.1041191
    26 schema:keywords Carlo
    27 GP predictions
    28 Gaussian process prediction
    29 MethodsWe
    30 PurposeThe use
    31 ResultsWe
    32 addition
    33 algorithm
    34 compensation
    35 compensation algorithm
    36 concept
    37 constant cutting velocity
    38 cut
    39 cutting velocity
    40 devices
    41 door
    42 enough time
    43 error
    44 focal point
    45 framework
    46 great potential
    47 history
    48 inaccuracy
    49 innovative opportunities
    50 laser focal point
    51 laser osteotome
    52 laser osteotomy
    53 laser source
    54 median position error
    55 median sternotomy
    56 methodology
    57 moment
    58 motion
    59 motion compensation
    60 motion compensation algorithm
    61 novel
    62 opportunities
    63 order
    64 osteotome
    65 osteotomy
    66 pain
    67 paper
    68 patients
    69 patterns
    70 performance
    71 planning algorithm
    72 point
    73 position
    74 position error
    75 potential
    76 prediction
    77 procedure
    78 process prediction
    79 recovery time
    80 related novels
    81 respiration-induced motion
    82 respiratory motion compensation
    83 results
    84 simulation framework
    85 simulation results
    86 source
    87 stability
    88 sternal stability
    89 sternotomy
    90 sternotomy procedures
    91 sternum
    92 successful compensation
    93 thorax
    94 threshold moment
    95 time
    96 today's methodologies
    97 trajectory planning algorithm
    98 use
    99 velocity
    100 schema:name Respiratory motion compensation for the robot-guided laser osteotome
    101 schema:pagination 1751-1762
    102 schema:productId N2249582e60ce43db92050cf62b144657
    103 N73be9c5856d74d94b98dff17b4a4ce9d
    104 Nca876adc34e040c9956bfbe813d54b58
    105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084031837
    106 https://doi.org/10.1007/s11548-017-1543-3
    107 schema:sdDatePublished 2022-12-01T06:37
    108 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    109 schema:sdPublisher N96a6ad905a11498b80f09ba9482877be
    110 schema:url https://doi.org/10.1007/s11548-017-1543-3
    111 sgo:license sg:explorer/license/
    112 sgo:sdDataset articles
    113 rdf:type schema:ScholarlyArticle
    114 N0e11a5cacbec404c9d5e7f1253c58a84 rdf:first sg:person.010024631132.03
    115 rdf:rest Nff3f841500a7436398a3c05abb3cc0f2
    116 N0fc77340f06e47eba95e55f1c8e51db1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    117 schema:name Laser Therapy
    118 rdf:type schema:DefinedTerm
    119 N10f3fe94662748a6baefae157bff3c0f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    120 schema:name Algorithms
    121 rdf:type schema:DefinedTerm
    122 N2249582e60ce43db92050cf62b144657 schema:name pubmed_id
    123 schema:value 28258401
    124 rdf:type schema:PropertyValue
    125 N6e58a56b8d56418cb9fc72a758063de2 schema:issueNumber 10
    126 rdf:type schema:PublicationIssue
    127 N73be9c5856d74d94b98dff17b4a4ce9d schema:name doi
    128 schema:value 10.1007/s11548-017-1543-3
    129 rdf:type schema:PropertyValue
    130 N7d43c7b78aa24aaaae79808cf1af0e7f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    131 schema:name Imaging, Three-Dimensional
    132 rdf:type schema:DefinedTerm
    133 N8728c55c7c1f432ea56a6298583033e5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    134 schema:name Humans
    135 rdf:type schema:DefinedTerm
    136 N96a6ad905a11498b80f09ba9482877be schema:name Springer Nature - SN SciGraph project
    137 rdf:type schema:Organization
    138 N9ed194b24a2a4a30aca7332ca35c0eb6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    139 schema:name Robotics
    140 rdf:type schema:DefinedTerm
    141 Na88a44a26af34d209900751746a40f52 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    142 schema:name Sternotomy
    143 rdf:type schema:DefinedTerm
    144 Nb9cc6797177444d780ef14fa76356a64 schema:volumeNumber 12
    145 rdf:type schema:PublicationVolume
    146 Nc1f00e19fc404614ac4de3191ff59bfe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    147 schema:name Motion
    148 rdf:type schema:DefinedTerm
    149 Nc779ae99d6574be9a78cbd1df5026aed schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    150 schema:name Computer Simulation
    151 rdf:type schema:DefinedTerm
    152 Nc8a248a9bbb744878f1daf2e3f22b572 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    153 schema:name Respiration
    154 rdf:type schema:DefinedTerm
    155 Nca876adc34e040c9956bfbe813d54b58 schema:name dimensions_id
    156 schema:value pub.1084031837
    157 rdf:type schema:PropertyValue
    158 Ne66514ab00094fffaadc3dc469235d28 rdf:first sg:person.014020325017.05
    159 rdf:rest N0e11a5cacbec404c9d5e7f1253c58a84
    160 Neb1ff8b91b5747e3a54b25cbb2ff3dff schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    161 schema:name Osteotomy
    162 rdf:type schema:DefinedTerm
    163 Nff3f841500a7436398a3c05abb3cc0f2 rdf:first sg:person.015501206005.19
    164 rdf:rest rdf:nil
    165 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    166 schema:name Medical and Health Sciences
    167 rdf:type schema:DefinedTerm
    168 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
    169 schema:name Clinical Sciences
    170 rdf:type schema:DefinedTerm
    171 sg:journal.1041191 schema:issn 1861-6410
    172 1861-6429
    173 schema:name International Journal of Computer Assisted Radiology and Surgery
    174 schema:publisher Springer Nature
    175 rdf:type schema:Periodical
    176 sg:person.010024631132.03 schema:affiliation grid-institutes:grid.6612.3
    177 schema:familyName Jud
    178 schema:givenName Christoph
    179 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010024631132.03
    180 rdf:type schema:Person
    181 sg:person.014020325017.05 schema:affiliation grid-institutes:grid.6612.3
    182 schema:familyName Giger
    183 schema:givenName Alina
    184 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014020325017.05
    185 rdf:type schema:Person
    186 sg:person.015501206005.19 schema:affiliation grid-institutes:grid.6612.3
    187 schema:familyName Cattin
    188 schema:givenName Philippe C.
    189 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015501206005.19
    190 rdf:type schema:Person
    191 sg:pub.10.1007/978-3-540-75759-7_81 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016500879
    192 https://doi.org/10.1007/978-3-540-75759-7_81
    193 rdf:type schema:CreativeWork
    194 sg:pub.10.1007/978-3-642-40763-5_14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006988604
    195 https://doi.org/10.1007/978-3-642-40763-5_14
    196 rdf:type schema:CreativeWork
    197 sg:pub.10.1007/s11548-009-0355-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045434258
    198 https://doi.org/10.1007/s11548-009-0355-5
    199 rdf:type schema:CreativeWork
    200 sg:pub.10.1186/1749-8090-4-19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007581515
    201 https://doi.org/10.1186/1749-8090-4-19
    202 rdf:type schema:CreativeWork
    203 grid-institutes:grid.6612.3 schema:alternateName Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123, Allschwil, Switzerland
    204 schema:name Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123, Allschwil, Switzerland
    205 rdf:type schema:Organization
     




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


    ...