Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12-13

AUTHORS

Grigorios-Aris Cheimariotis, Mariam Al-Mashat, Kostas Haris, Anthony H. Aletras, Jonas Jögi, Marika Bajc, Nicolaos Maglaveras, Einar Heiberg

ABSTRACT

ObjectiveImage segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes.MethodsA total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images.ResultsThe Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p < 0.001) in SPECT. There were similar observations when comparing reference volumes from CT and manual delineations in SPECT images, left lung (bias was − 10 ± 491, R = 0.60, p = 0.005) right lung (bias 36 ± 524 ml, R = 0.62, p = 0.004).ConclusionAutomated segmentation on SPECT images are on par with manual segmentation on SPECT images. Relative large volumetric differences between manual delineations of functional SPECT images and anatomical CT images confirms that lung segmentation of functional SPECT images is a challenging task. The current algorithm is a first step towards automatic quantification of wide range of measurements. More... »

PAGES

94-104

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12149-017-1223-y

DOI

http://dx.doi.org/10.1007/s12149-017-1223-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1099691674

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29236220


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/2008/1102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Cardiorespiratory Medicine and Haematology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Automation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Image Processing, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Lung", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Pattern Recognition, Automated", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reference Standards", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, Emission-Computed, Single-Photon", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, X-Ray Computed", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece", 
          "id": "http://www.grid.ac/institutes/grid.4793.9", 
          "name": [
            "Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cheimariotis", 
        "givenName": "Grigorios-Aris", 
        "id": "sg:person.01235272714.71", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235272714.71"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Clinical Sciences Lund, Clinical Physiology, Sk\u00e5ne University Hospital, Lund University, Lund, Sweden", 
          "id": "http://www.grid.ac/institutes/grid.4514.4", 
          "name": [
            "Department of Clinical Sciences Lund, Clinical Physiology, Sk\u00e5ne University Hospital, Lund University, Lund, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Al-Mashat", 
        "givenName": "Mariam", 
        "id": "sg:person.01014551272.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01014551272.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece", 
          "id": "http://www.grid.ac/institutes/grid.4793.9", 
          "name": [
            "Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Haris", 
        "givenName": "Kostas", 
        "id": "sg:person.014145372501.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014145372501.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Clinical Sciences Lund, Clinical Physiology, Sk\u00e5ne University Hospital, Lund University, Lund, Sweden", 
          "id": "http://www.grid.ac/institutes/grid.4514.4", 
          "name": [
            "Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece", 
            "Department of Clinical Sciences Lund, Clinical Physiology, Sk\u00e5ne University Hospital, Lund University, Lund, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Aletras", 
        "givenName": "Anthony H.", 
        "id": "sg:person.01044222552.06", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01044222552.06"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Clinical Sciences Lund, Clinical Physiology, Sk\u00e5ne University Hospital, Lund University, Lund, Sweden", 
          "id": "http://www.grid.ac/institutes/grid.4514.4", 
          "name": [
            "Department of Clinical Sciences Lund, Clinical Physiology, Sk\u00e5ne University Hospital, Lund University, Lund, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "J\u00f6gi", 
        "givenName": "Jonas", 
        "id": "sg:person.0604671751.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0604671751.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Clinical Sciences Lund, Clinical Physiology, Sk\u00e5ne University Hospital, Lund University, Lund, Sweden", 
          "id": "http://www.grid.ac/institutes/grid.4514.4", 
          "name": [
            "Department of Clinical Sciences Lund, Clinical Physiology, Sk\u00e5ne University Hospital, Lund University, Lund, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bajc", 
        "givenName": "Marika", 
        "id": "sg:person.01170740331.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01170740331.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece", 
          "id": "http://www.grid.ac/institutes/grid.4793.9", 
          "name": [
            "Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Maglaveras", 
        "givenName": "Nicolaos", 
        "id": "sg:person.011101512454.71", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011101512454.71"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Clinical Physiology, Lund University Hospital, 22185, Lund, Sweden", 
          "id": "http://www.grid.ac/institutes/grid.411843.b", 
          "name": [
            "Department of Clinical Sciences Lund, Clinical Physiology, Sk\u00e5ne University Hospital, Lund University, Lund, Sweden", 
            "Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden", 
            "Department of Clinical Physiology, Lund University Hospital, 22185, Lund, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Heiberg", 
        "givenName": "Einar", 
        "id": "sg:person.0643341416.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0643341416.42"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s11239-014-1097-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052047385", 
          "https://doi.org/10.1007/s11239-014-1097-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12149-014-0913-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038647134", 
          "https://doi.org/10.1007/s12149-014-0913-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00259-011-1757-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012077845", 
          "https://doi.org/10.1007/s00259-011-1757-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00259-009-1170-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009319143", 
          "https://doi.org/10.1007/s00259-009-1170-5"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-12-13", 
    "datePublishedReg": "2017-12-13", 
    "description": "ObjectiveImage segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes.MethodsA total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images.ResultsThe Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83\u2009\u00b1\u20090.04% for the right and 0.82\u2009\u00b1\u20090.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R\u2009=\u20090.53, p\u2009=\u20090.02) and left lung (R\u2009=\u20090.69, p\u2009<\u20090.001) in SPECT. There were similar observations when comparing reference volumes from CT and manual delineations in SPECT images, left lung (bias was \u2212\u200910\u2009\u00b1\u2009491, R\u2009=\u20090.60, p\u2009=\u20090.005) right lung (bias 36\u2009\u00b1\u2009524\u00a0ml, R\u2009=\u20090.62, p\u2009=\u20090.004).ConclusionAutomated segmentation on SPECT images are on par with manual segmentation on SPECT images. Relative large volumetric differences between manual delineations of functional SPECT images and anatomical CT images confirms that lung segmentation of functional SPECT images is a challenging task. The current algorithm is a first step towards automatic quantification of wide range of measurements.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s12149-017-1223-y", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1099601", 
        "issn": [
          "0914-7187", 
          "1864-6433"
        ], 
        "name": "Annals of Nuclear Medicine", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "32"
      }
    ], 
    "keywords": [
      "SPECT images", 
      "low-dose CT", 
      "CT images", 
      "anatomical CT images", 
      "lung function", 
      "left lung", 
      "right lung", 
      "SPECT examination", 
      "lung", 
      "perfusion SPECT images", 
      "volumetric differences", 
      "CT", 
      "significant differences", 
      "lung shape", 
      "corresponding CT images", 
      "manual delineation", 
      "low-dose CT images", 
      "reference volume", 
      "reference delineations", 
      "patients", 
      "Similar observations", 
      "gamma camera system", 
      "tomography volumes", 
      "ventilation", 
      "SPECT", 
      "delineation", 
      "total", 
      "differences", 
      "volume", 
      "examination", 
      "subjects", 
      "lung segmentation", 
      "aim", 
      "automatic quantification", 
      "manual segmentation", 
      "essential step", 
      "new tool", 
      "study", 
      "automatic delineation", 
      "Dice coefficient", 
      "function", 
      "extent", 
      "quantification", 
      "rights", 
      "model", 
      "results", 
      "first step", 
      "automatic segmentation", 
      "images", 
      "Automatic Lung Segmentation", 
      "wide range", 
      "tool", 
      "observations", 
      "reference", 
      "step", 
      "measurements", 
      "par", 
      "phase", 
      "task", 
      "system", 
      "range", 
      "current algorithms", 
      "camera system", 
      "training phase", 
      "segmentation", 
      "coefficient", 
      "shape", 
      "challenging task", 
      "shape model", 
      "Active Shape Model", 
      "algorithm", 
      "image segmentation"
    ], 
    "name": "Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT", 
    "pagination": "94-104", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1099691674"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12149-017-1223-y"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29236220"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12149-017-1223-y", 
      "https://app.dimensions.ai/details/publication/pub.1099691674"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-10-01T06:42", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_723.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s12149-017-1223-y"
  }
]
 

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/s12149-017-1223-y'

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/s12149-017-1223-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12149-017-1223-y'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12149-017-1223-y'


 

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

243 TRIPLES      21 PREDICATES      110 URIs      98 LITERALS      16 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12149-017-1223-y schema:about N12f7658c5bb24d789580c24aa6ac8400
2 N3291520be37c4f059d62362433662ec1
3 N5ae3313fb9d14dd2ac02c2d6af295350
4 Na5dbb368783243a4a1777f237e482674
5 Nae386a687f474ba69fb621210ac8f446
6 Nba6c6b546f234c738bd37d40e98ebc55
7 Nd52961882ca647b0a15857ba59771f4b
8 Ndd5d9ad1c1e64354bdbc9fd1f3671a8e
9 Nec957f0f038d46dda1528b2911e83430
10 anzsrc-for:11
11 anzsrc-for:1102
12 schema:author N04bc5893a8494d23a1198b613c50f9ff
13 schema:citation sg:pub.10.1007/s00259-009-1170-5
14 sg:pub.10.1007/s00259-011-1757-5
15 sg:pub.10.1007/s11239-014-1097-y
16 sg:pub.10.1007/s12149-014-0913-y
17 schema:datePublished 2017-12-13
18 schema:datePublishedReg 2017-12-13
19 schema:description ObjectiveImage segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes.MethodsA total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images.ResultsThe Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p < 0.001) in SPECT. There were similar observations when comparing reference volumes from CT and manual delineations in SPECT images, left lung (bias was − 10 ± 491, R = 0.60, p = 0.005) right lung (bias 36 ± 524 ml, R = 0.62, p = 0.004).ConclusionAutomated segmentation on SPECT images are on par with manual segmentation on SPECT images. Relative large volumetric differences between manual delineations of functional SPECT images and anatomical CT images confirms that lung segmentation of functional SPECT images is a challenging task. The current algorithm is a first step towards automatic quantification of wide range of measurements.
20 schema:genre article
21 schema:isAccessibleForFree true
22 schema:isPartOf Ndff1cee8c78c423d8f770f261195d4ae
23 Nfe46f06d8baa4819b99b88477946fdb3
24 sg:journal.1099601
25 schema:keywords Active Shape Model
26 Automatic Lung Segmentation
27 CT
28 CT images
29 Dice coefficient
30 SPECT
31 SPECT examination
32 SPECT images
33 Similar observations
34 aim
35 algorithm
36 anatomical CT images
37 automatic delineation
38 automatic quantification
39 automatic segmentation
40 camera system
41 challenging task
42 coefficient
43 corresponding CT images
44 current algorithms
45 delineation
46 differences
47 essential step
48 examination
49 extent
50 first step
51 function
52 gamma camera system
53 image segmentation
54 images
55 left lung
56 low-dose CT
57 low-dose CT images
58 lung
59 lung function
60 lung segmentation
61 lung shape
62 manual delineation
63 manual segmentation
64 measurements
65 model
66 new tool
67 observations
68 par
69 patients
70 perfusion SPECT images
71 phase
72 quantification
73 range
74 reference
75 reference delineations
76 reference volume
77 results
78 right lung
79 rights
80 segmentation
81 shape
82 shape model
83 significant differences
84 step
85 study
86 subjects
87 system
88 task
89 tomography volumes
90 tool
91 total
92 training phase
93 ventilation
94 volume
95 volumetric differences
96 wide range
97 schema:name Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT
98 schema:pagination 94-104
99 schema:productId N2ac1deac144347f6947d45faac0744cf
100 N466c4b6d79274d328646917a1a354107
101 Ndb676ad715534d778a40a2d8d8380294
102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099691674
103 https://doi.org/10.1007/s12149-017-1223-y
104 schema:sdDatePublished 2022-10-01T06:42
105 schema:sdLicense https://scigraph.springernature.com/explorer/license/
106 schema:sdPublisher Nea90f16db2b64d9d9442a0e7ec8c9744
107 schema:url https://doi.org/10.1007/s12149-017-1223-y
108 sgo:license sg:explorer/license/
109 sgo:sdDataset articles
110 rdf:type schema:ScholarlyArticle
111 N04bc5893a8494d23a1198b613c50f9ff rdf:first sg:person.01235272714.71
112 rdf:rest N42d054ff70c541dab08789ee0583fb1f
113 N12f7658c5bb24d789580c24aa6ac8400 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Pattern Recognition, Automated
115 rdf:type schema:DefinedTerm
116 N2ac1deac144347f6947d45faac0744cf schema:name dimensions_id
117 schema:value pub.1099691674
118 rdf:type schema:PropertyValue
119 N3291520be37c4f059d62362433662ec1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Automation
121 rdf:type schema:DefinedTerm
122 N42d054ff70c541dab08789ee0583fb1f rdf:first sg:person.01014551272.45
123 rdf:rest Naec343143d48472198101dfa893a1223
124 N466c4b6d79274d328646917a1a354107 schema:name doi
125 schema:value 10.1007/s12149-017-1223-y
126 rdf:type schema:PropertyValue
127 N5ae3313fb9d14dd2ac02c2d6af295350 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Tomography, X-Ray Computed
129 rdf:type schema:DefinedTerm
130 N71eb884254b8496099ae6ed2c06b9de3 rdf:first sg:person.01170740331.16
131 rdf:rest Nc85f206d4f6640b689802fe6a41c842b
132 Na5dbb368783243a4a1777f237e482674 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
133 schema:name Image Processing, Computer-Assisted
134 rdf:type schema:DefinedTerm
135 Nae386a687f474ba69fb621210ac8f446 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Algorithms
137 rdf:type schema:DefinedTerm
138 Naec343143d48472198101dfa893a1223 rdf:first sg:person.014145372501.15
139 rdf:rest Ncebe553733e0406285f294a7c4fa39d9
140 Nb2468d6d9a7044af9388b5b7dd73d349 rdf:first sg:person.0604671751.19
141 rdf:rest N71eb884254b8496099ae6ed2c06b9de3
142 Nba6c6b546f234c738bd37d40e98ebc55 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Humans
144 rdf:type schema:DefinedTerm
145 Nc85f206d4f6640b689802fe6a41c842b rdf:first sg:person.011101512454.71
146 rdf:rest Nfeb2efbce58647789c316215fa589aef
147 Ncebe553733e0406285f294a7c4fa39d9 rdf:first sg:person.01044222552.06
148 rdf:rest Nb2468d6d9a7044af9388b5b7dd73d349
149 Nd52961882ca647b0a15857ba59771f4b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
150 schema:name Lung
151 rdf:type schema:DefinedTerm
152 Ndb676ad715534d778a40a2d8d8380294 schema:name pubmed_id
153 schema:value 29236220
154 rdf:type schema:PropertyValue
155 Ndd5d9ad1c1e64354bdbc9fd1f3671a8e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
156 schema:name Reference Standards
157 rdf:type schema:DefinedTerm
158 Ndff1cee8c78c423d8f770f261195d4ae schema:volumeNumber 32
159 rdf:type schema:PublicationVolume
160 Nea90f16db2b64d9d9442a0e7ec8c9744 schema:name Springer Nature - SN SciGraph project
161 rdf:type schema:Organization
162 Nec957f0f038d46dda1528b2911e83430 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
163 schema:name Tomography, Emission-Computed, Single-Photon
164 rdf:type schema:DefinedTerm
165 Nfe46f06d8baa4819b99b88477946fdb3 schema:issueNumber 2
166 rdf:type schema:PublicationIssue
167 Nfeb2efbce58647789c316215fa589aef rdf:first sg:person.0643341416.42
168 rdf:rest rdf:nil
169 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
170 schema:name Medical and Health Sciences
171 rdf:type schema:DefinedTerm
172 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
173 schema:name Cardiorespiratory Medicine and Haematology
174 rdf:type schema:DefinedTerm
175 sg:journal.1099601 schema:issn 0914-7187
176 1864-6433
177 schema:name Annals of Nuclear Medicine
178 schema:publisher Springer Nature
179 rdf:type schema:Periodical
180 sg:person.01014551272.45 schema:affiliation grid-institutes:grid.4514.4
181 schema:familyName Al-Mashat
182 schema:givenName Mariam
183 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01014551272.45
184 rdf:type schema:Person
185 sg:person.01044222552.06 schema:affiliation grid-institutes:grid.4514.4
186 schema:familyName Aletras
187 schema:givenName Anthony H.
188 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01044222552.06
189 rdf:type schema:Person
190 sg:person.011101512454.71 schema:affiliation grid-institutes:grid.4793.9
191 schema:familyName Maglaveras
192 schema:givenName Nicolaos
193 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011101512454.71
194 rdf:type schema:Person
195 sg:person.01170740331.16 schema:affiliation grid-institutes:grid.4514.4
196 schema:familyName Bajc
197 schema:givenName Marika
198 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01170740331.16
199 rdf:type schema:Person
200 sg:person.01235272714.71 schema:affiliation grid-institutes:grid.4793.9
201 schema:familyName Cheimariotis
202 schema:givenName Grigorios-Aris
203 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235272714.71
204 rdf:type schema:Person
205 sg:person.014145372501.15 schema:affiliation grid-institutes:grid.4793.9
206 schema:familyName Haris
207 schema:givenName Kostas
208 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014145372501.15
209 rdf:type schema:Person
210 sg:person.0604671751.19 schema:affiliation grid-institutes:grid.4514.4
211 schema:familyName Jögi
212 schema:givenName Jonas
213 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0604671751.19
214 rdf:type schema:Person
215 sg:person.0643341416.42 schema:affiliation grid-institutes:grid.411843.b
216 schema:familyName Heiberg
217 schema:givenName Einar
218 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0643341416.42
219 rdf:type schema:Person
220 sg:pub.10.1007/s00259-009-1170-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009319143
221 https://doi.org/10.1007/s00259-009-1170-5
222 rdf:type schema:CreativeWork
223 sg:pub.10.1007/s00259-011-1757-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012077845
224 https://doi.org/10.1007/s00259-011-1757-5
225 rdf:type schema:CreativeWork
226 sg:pub.10.1007/s11239-014-1097-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1052047385
227 https://doi.org/10.1007/s11239-014-1097-y
228 rdf:type schema:CreativeWork
229 sg:pub.10.1007/s12149-014-0913-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1038647134
230 https://doi.org/10.1007/s12149-014-0913-y
231 rdf:type schema:CreativeWork
232 grid-institutes:grid.411843.b schema:alternateName Department of Clinical Physiology, Lund University Hospital, 22185, Lund, Sweden
233 schema:name Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
234 Department of Clinical Physiology, Lund University Hospital, 22185, Lund, Sweden
235 Department of Clinical Sciences Lund, Clinical Physiology, Skåne University Hospital, Lund University, Lund, Sweden
236 rdf:type schema:Organization
237 grid-institutes:grid.4514.4 schema:alternateName Department of Clinical Sciences Lund, Clinical Physiology, Skåne University Hospital, Lund University, Lund, Sweden
238 schema:name Department of Clinical Sciences Lund, Clinical Physiology, Skåne University Hospital, Lund University, Lund, Sweden
239 Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
240 rdf:type schema:Organization
241 grid-institutes:grid.4793.9 schema:alternateName Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
242 schema:name Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
243 rdf:type schema:Organization
 




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


...