Automatic Fetal Measurements in Ultrasound Using Constrained Probabilistic Boosting Tree View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2007

AUTHORS

Gustavo Carneiro , Bogdan Georgescu , Sara Good , Dorin Comaniciu

ABSTRACT

Automatic delineation and robust measurement of fetal anat-omical structures in 2D ultrasound images is a challenging task due to the complexity of the object appearance, noise, shadows, and quantity of information to be processed. Previous solutions rely on explicit encoding of prior knowledge and formulate the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are known to be limited by the validity of the underlying assumptions and cannot capture complex structure appearances. We propose a novel system for fast automatic obstetric measurements by directly exploiting a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns to distinguish between the appearance of the object of interest and background by training a discriminative constrained probabilistic boosting tree classifier. This system is able to handle previously unsolved problems in this domain, such as the effective segmentation of fetal abdomens. We show results on fully automatic measurement of head circumference, biparietal diameter, abdominal circumference and femur length. Unparalleled extensive experiments show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer. More... »

PAGES

571-579

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-75759-7_69

DOI

http://dx.doi.org/10.1007/978-3-540-75759-7_69

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1030387286

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18044614


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Artificial Intelligence", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Interpretation, Statistical", 
        "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": "Pattern Recognition, Automated", 
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ultrasonography, Prenatal", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Whole Body Imaging", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Siemens Corporate Research, Integrated Data Systems Dept., Princeton, NJ, USA", 
          "id": "http://www.grid.ac/institutes/grid.419233.e", 
          "name": [
            "Siemens Corporate Research, Integrated Data Systems Dept., Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Carneiro", 
        "givenName": "Gustavo", 
        "id": "sg:person.07656565701.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07656565701.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Siemens Corporate Research, Integrated Data Systems Dept., Princeton, NJ, USA", 
          "id": "http://www.grid.ac/institutes/grid.419233.e", 
          "name": [
            "Siemens Corporate Research, Integrated Data Systems Dept., Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Georgescu", 
        "givenName": "Bogdan", 
        "id": "sg:person.0703547214.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0703547214.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Siemens Medical Solutions, Innovations Ultrasound Div., Mountain View, CA, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Siemens Medical Solutions, Innovations Ultrasound Div., Mountain View, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Good", 
        "givenName": "Sara", 
        "id": "sg:person.0614045271.61", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0614045271.61"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Siemens Corporate Research, Integrated Data Systems Dept., Princeton, NJ, USA", 
          "id": "http://www.grid.ac/institutes/grid.419233.e", 
          "name": [
            "Siemens Corporate Research, Integrated Data Systems Dept., Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Comaniciu", 
        "givenName": "Dorin", 
        "id": "sg:person.01066111014.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066111014.77"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2007", 
    "datePublishedReg": "2007-01-01", 
    "description": "Automatic delineation and robust measurement of fetal anat-omical structures in 2D ultrasound images is a challenging task due to the complexity of the object appearance, noise, shadows, and quantity of information to be processed. Previous solutions rely on explicit encoding of prior knowledge and formulate the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are known to be limited by the validity of the underlying assumptions and cannot capture complex structure appearances. We propose a novel system for fast automatic obstetric measurements by directly exploiting a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns to distinguish between the appearance of the object of interest and background by training a discriminative constrained probabilistic boosting tree classifier. This system is able to handle previously unsolved problems in this domain, such as the effective segmentation of fetal abdomens. We show results on fully automatic measurement of head circumference, biparietal diameter, abdominal circumference and femur length. Unparalleled extensive experiments show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.", 
    "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.10.1007/978-3-540-75759-7_69", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-75758-0", 
        "978-3-540-75759-7"
      ], 
      "name": "Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2007", 
      "type": "Book"
    }, 
    "keywords": [
      "obstetric measurements", 
      "probabilistic boosting tree", 
      "probabilistic boosting tree classifier", 
      "terms of segmentation", 
      "object of interest", 
      "ultrasound images", 
      "fetal anatomical structures", 
      "perceptual grouping task", 
      "Extensive experiments", 
      "boosting tree", 
      "object appearance", 
      "effective segmentation", 
      "quantity of information", 
      "large database", 
      "PC computer", 
      "challenging task", 
      "tree classifier", 
      "previous solutions", 
      "automatic delineation", 
      "explicit encoding", 
      "accuracy of experts", 
      "prior knowledge", 
      "segmentation", 
      "automatic measurement", 
      "novel system", 
      "structure appearance", 
      "task", 
      "images", 
      "anatomical structures", 
      "unsolved problem", 
      "classifier", 
      "experts", 
      "system", 
      "computer", 
      "discriminative", 
      "complexity", 
      "objects", 
      "encoding", 
      "robust measurement", 
      "database", 
      "information", 
      "accuracy", 
      "noise", 
      "method", 
      "shadow", 
      "domain", 
      "variational approach", 
      "trees", 
      "solution", 
      "knowledge", 
      "experiments", 
      "interest", 
      "terms", 
      "assumption", 
      "structure", 
      "results", 
      "delineation", 
      "validity", 
      "background", 
      "appearance", 
      "measurements", 
      "quantity", 
      "length", 
      "problem", 
      "biparietal diameter", 
      "ultrasound", 
      "head circumference", 
      "fetal measurements", 
      "approach", 
      "half", 
      "femur length", 
      "abdominal circumference", 
      "diameter", 
      "circumference", 
      "abdomens", 
      "fetal anat-omical structures", 
      "anat-omical structures", 
      "grouping task", 
      "complex structure appearances", 
      "fast automatic obstetric measurements", 
      "automatic obstetric measurements", 
      "boosting tree classifier", 
      "fetal abdomens", 
      "Unparalleled extensive experiments", 
      "standard dual-core PC computer", 
      "dual-core PC computer", 
      "Automatic Fetal Measurements"
    ], 
    "name": "Automatic Fetal Measurements in Ultrasound Using Constrained Probabilistic Boosting Tree", 
    "pagination": "571-579", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1030387286"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-75759-7_69"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "18044614"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-75759-7_69", 
      "https://app.dimensions.ai/details/publication/pub.1030387286"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:23", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/chapter/chapter_404.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-540-75759-7_69"
  }
]
 

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.10.1007/978-3-540-75759-7_69'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-75759-7_69'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-75759-7_69'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-75759-7_69'


 

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

233 TRIPLES      23 PREDICATES      126 URIs      119 LITERALS      20 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-75759-7_69 schema:about N0a2cb23e6eaf46dcb43676fa7ea13975
2 N300f406de1d145ce97d4c4438d0e3395
3 N7cd645fe73c14bf9b28168cb79dab9e3
4 N83202c7ef93c490484032ee9d3e5f2ab
5 N8c38ba1df6bc4162bf8c3677bde31ab0
6 Na4111e9adb6b4c95873843ae63d9e4b6
7 Na536bee29a00471b8c03579a60543227
8 Naf76950b1c2a4cf1b09d40016c981886
9 Nca8e83c0c4bc4bb89e88515c5d557d39
10 Nd3aaa70d63214125a55bd0dc818c7e9a
11 Ne0e1442dd7884ef6a1a2a16f4a38a393
12 Nf17fb757450346b086b7ad3be74de779
13 anzsrc-for:08
14 anzsrc-for:0801
15 schema:author N4e2d66ac08a64d69abc0568965a6c964
16 schema:datePublished 2007
17 schema:datePublishedReg 2007-01-01
18 schema:description Automatic delineation and robust measurement of fetal anat-omical structures in 2D ultrasound images is a challenging task due to the complexity of the object appearance, noise, shadows, and quantity of information to be processed. Previous solutions rely on explicit encoding of prior knowledge and formulate the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are known to be limited by the validity of the underlying assumptions and cannot capture complex structure appearances. We propose a novel system for fast automatic obstetric measurements by directly exploiting a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns to distinguish between the appearance of the object of interest and background by training a discriminative constrained probabilistic boosting tree classifier. This system is able to handle previously unsolved problems in this domain, such as the effective segmentation of fetal abdomens. We show results on fully automatic measurement of head circumference, biparietal diameter, abdominal circumference and femur length. Unparalleled extensive experiments show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.
19 schema:editor N21df1a0e2c5b4121aa05929c7416a4cc
20 schema:genre chapter
21 schema:inLanguage en
22 schema:isAccessibleForFree true
23 schema:isPartOf N8ad95ed5680441b1a46bad0543cecff7
24 schema:keywords Automatic Fetal Measurements
25 Extensive experiments
26 PC computer
27 Unparalleled extensive experiments
28 abdomens
29 abdominal circumference
30 accuracy
31 accuracy of experts
32 anat-omical structures
33 anatomical structures
34 appearance
35 approach
36 assumption
37 automatic delineation
38 automatic measurement
39 automatic obstetric measurements
40 background
41 biparietal diameter
42 boosting tree
43 boosting tree classifier
44 challenging task
45 circumference
46 classifier
47 complex structure appearances
48 complexity
49 computer
50 database
51 delineation
52 diameter
53 discriminative
54 domain
55 dual-core PC computer
56 effective segmentation
57 encoding
58 experiments
59 experts
60 explicit encoding
61 fast automatic obstetric measurements
62 femur length
63 fetal abdomens
64 fetal anat-omical structures
65 fetal anatomical structures
66 fetal measurements
67 grouping task
68 half
69 head circumference
70 images
71 information
72 interest
73 knowledge
74 large database
75 length
76 measurements
77 method
78 noise
79 novel system
80 object appearance
81 object of interest
82 objects
83 obstetric measurements
84 perceptual grouping task
85 previous solutions
86 prior knowledge
87 probabilistic boosting tree
88 probabilistic boosting tree classifier
89 problem
90 quantity
91 quantity of information
92 results
93 robust measurement
94 segmentation
95 shadow
96 solution
97 standard dual-core PC computer
98 structure
99 structure appearance
100 system
101 task
102 terms
103 terms of segmentation
104 tree classifier
105 trees
106 ultrasound
107 ultrasound images
108 unsolved problem
109 validity
110 variational approach
111 schema:name Automatic Fetal Measurements in Ultrasound Using Constrained Probabilistic Boosting Tree
112 schema:pagination 571-579
113 schema:productId N620720fd0cc948318e3bd2cc6cbcab98
114 Naa73743a3e7d4a6ab13d5bb3b9b757f0
115 Nd307fcbf6d84425d818b34fd2feffa77
116 schema:publisher N0223525be2b742f990dd130aa25c0ffa
117 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030387286
118 https://doi.org/10.1007/978-3-540-75759-7_69
119 schema:sdDatePublished 2022-01-01T19:23
120 schema:sdLicense https://scigraph.springernature.com/explorer/license/
121 schema:sdPublisher Nc33e42524a1b430ebff2f02f74c7dae0
122 schema:url https://doi.org/10.1007/978-3-540-75759-7_69
123 sgo:license sg:explorer/license/
124 sgo:sdDataset chapters
125 rdf:type schema:Chapter
126 N0223525be2b742f990dd130aa25c0ffa schema:name Springer Nature
127 rdf:type schema:Organisation
128 N0a13dd97a7aa4bca832c969e24a26130 rdf:first N1fb36a38cdb34fb2b7e23aba1c4adbf7
129 rdf:rest rdf:nil
130 N0a2cb23e6eaf46dcb43676fa7ea13975 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Artificial Intelligence
132 rdf:type schema:DefinedTerm
133 N0da3495e60ed477b8f48551a85b70faa schema:familyName Ourselin
134 schema:givenName Sébastien
135 rdf:type schema:Person
136 N14fea7e73f964a0090f9ed761784688e rdf:first sg:person.01066111014.77
137 rdf:rest rdf:nil
138 N1fb36a38cdb34fb2b7e23aba1c4adbf7 schema:familyName Maeder
139 schema:givenName Anthony
140 rdf:type schema:Person
141 N21df1a0e2c5b4121aa05929c7416a4cc rdf:first N95b6acc7ded344b9b1ea02663f0529ea
142 rdf:rest Neb373e1c78b1412da58f359842e9ce35
143 N22fea80c569f4bfbab8f11092b0493cf rdf:first sg:person.0703547214.37
144 rdf:rest Na0f66cdebf36401e9eb4c81b2ef03f38
145 N300f406de1d145ce97d4c4438d0e3395 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
146 schema:name Image Enhancement
147 rdf:type schema:DefinedTerm
148 N4e2d66ac08a64d69abc0568965a6c964 rdf:first sg:person.07656565701.55
149 rdf:rest N22fea80c569f4bfbab8f11092b0493cf
150 N620720fd0cc948318e3bd2cc6cbcab98 schema:name dimensions_id
151 schema:value pub.1030387286
152 rdf:type schema:PropertyValue
153 N7cd645fe73c14bf9b28168cb79dab9e3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
154 schema:name Imaging, Three-Dimensional
155 rdf:type schema:DefinedTerm
156 N83202c7ef93c490484032ee9d3e5f2ab schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name Whole Body Imaging
158 rdf:type schema:DefinedTerm
159 N8ad95ed5680441b1a46bad0543cecff7 schema:isbn 978-3-540-75758-0
160 978-3-540-75759-7
161 schema:name Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
162 rdf:type schema:Book
163 N8c38ba1df6bc4162bf8c3677bde31ab0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
164 schema:name Sensitivity and Specificity
165 rdf:type schema:DefinedTerm
166 N95b6acc7ded344b9b1ea02663f0529ea schema:familyName Ayache
167 schema:givenName Nicholas
168 rdf:type schema:Person
169 Na0f66cdebf36401e9eb4c81b2ef03f38 rdf:first sg:person.0614045271.61
170 rdf:rest N14fea7e73f964a0090f9ed761784688e
171 Na4111e9adb6b4c95873843ae63d9e4b6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
172 schema:name Data Interpretation, Statistical
173 rdf:type schema:DefinedTerm
174 Na536bee29a00471b8c03579a60543227 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
175 schema:name Image Interpretation, Computer-Assisted
176 rdf:type schema:DefinedTerm
177 Naa73743a3e7d4a6ab13d5bb3b9b757f0 schema:name doi
178 schema:value 10.1007/978-3-540-75759-7_69
179 rdf:type schema:PropertyValue
180 Naf76950b1c2a4cf1b09d40016c981886 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
181 schema:name Humans
182 rdf:type schema:DefinedTerm
183 Nc33e42524a1b430ebff2f02f74c7dae0 schema:name Springer Nature - SN SciGraph project
184 rdf:type schema:Organization
185 Nca8e83c0c4bc4bb89e88515c5d557d39 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
186 schema:name Ultrasonography, Prenatal
187 rdf:type schema:DefinedTerm
188 Nd307fcbf6d84425d818b34fd2feffa77 schema:name pubmed_id
189 schema:value 18044614
190 rdf:type schema:PropertyValue
191 Nd3aaa70d63214125a55bd0dc818c7e9a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
192 schema:name Reproducibility of Results
193 rdf:type schema:DefinedTerm
194 Ne0e1442dd7884ef6a1a2a16f4a38a393 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
195 schema:name Algorithms
196 rdf:type schema:DefinedTerm
197 Neb373e1c78b1412da58f359842e9ce35 rdf:first N0da3495e60ed477b8f48551a85b70faa
198 rdf:rest N0a13dd97a7aa4bca832c969e24a26130
199 Nf17fb757450346b086b7ad3be74de779 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
200 schema:name Pattern Recognition, Automated
201 rdf:type schema:DefinedTerm
202 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
203 schema:name Information and Computing Sciences
204 rdf:type schema:DefinedTerm
205 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
206 schema:name Artificial Intelligence and Image Processing
207 rdf:type schema:DefinedTerm
208 sg:person.01066111014.77 schema:affiliation grid-institutes:grid.419233.e
209 schema:familyName Comaniciu
210 schema:givenName Dorin
211 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066111014.77
212 rdf:type schema:Person
213 sg:person.0614045271.61 schema:affiliation grid-institutes:None
214 schema:familyName Good
215 schema:givenName Sara
216 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0614045271.61
217 rdf:type schema:Person
218 sg:person.0703547214.37 schema:affiliation grid-institutes:grid.419233.e
219 schema:familyName Georgescu
220 schema:givenName Bogdan
221 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0703547214.37
222 rdf:type schema:Person
223 sg:person.07656565701.55 schema:affiliation grid-institutes:grid.419233.e
224 schema:familyName Carneiro
225 schema:givenName Gustavo
226 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07656565701.55
227 rdf:type schema:Person
228 grid-institutes:None schema:alternateName Siemens Medical Solutions, Innovations Ultrasound Div., Mountain View, CA, USA
229 schema:name Siemens Medical Solutions, Innovations Ultrasound Div., Mountain View, CA, USA
230 rdf:type schema:Organization
231 grid-institutes:grid.419233.e schema:alternateName Siemens Corporate Research, Integrated Data Systems Dept., Princeton, NJ, USA
232 schema:name Siemens Corporate Research, Integrated Data Systems Dept., Princeton, NJ, USA
233 rdf:type schema:Organization
 




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


...