Ontology type: schema:ScholarlyArticle
2016-03
AUTHORSSergio E. Martinez-Herrera, Yannick Benezeth, Matthieu Boffety, Jean-François Emile, Franck Marzani, Dominique Lamarque, François Goudail
ABSTRACTGastric cancer is one of the fifth most deadly cancers worldwide. Nowadays the diagnosis is performed through gastroendoscopy under white light and histological analysis. However, the precancerous lesions are multifocalized and present low differences with respect to healthy tissue. Several systems have been proposed based on light tissue interaction to improve the visualization of malignancies. However, these systems are limited to few wavelengths. In this paper, we propose a minimally invasive technique based on multispectral imaging and a methodology to identify malignancies in the stomach. We developed a multispectral gastroendoscopic system compatible with current gastroendoscopes, where only the illumination is changed. The spectra are extracted from the acquired multispectral images in order to compute statistical features that are used to classify the data in two classes: healthy and malignant. The features are ranked by pooled variance t test to train three classifiers. Neural networks using generalized relevance learning vector quantization, support vector machine (SVM) with a Gaussian kernel and k-nn are evaluated using leave one patient out cross-validation. Taking into consideration the data collected in this work, the quantitative results from the classification using SVM show high accuracy and sensitivity using a low number of features. These results show the ability to discriminate malignancies of the gastric tissue. Therefore, multispectral imaging could help in the identification of malignancies during gastroendoscopy. More... »
PAGES455-462
http://scigraph.springernature.com/pub.10.1007/s11760-015-0779-z
DOIhttp://dx.doi.org/10.1007/s11760-015-0779-z
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1001026587
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Versailles Saint-Quentin-en-Yvelines University",
"id": "https://www.grid.ac/institutes/grid.12832.3a",
"name": [
"Institut d\u2019Optique Graduate School, 2 av. Augustin Fresnel, 91127, Palaiseau, France",
"Universit\u00e9 de Versailles-Saint-Quentin-en-Yvelines, 55 Avenue de Paris, 78000, Versailles, France"
],
"type": "Organization"
},
"familyName": "Martinez-Herrera",
"givenName": "Sergio E.",
"id": "sg:person.013554503013.55",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013554503013.55"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Burgundy",
"id": "https://www.grid.ac/institutes/grid.5613.1",
"name": [
"Le2i - UMR CNRS 6306, Universit\u00e9 de Bourgogne, 9 av. Alain Savary, 21078, Dijon, France"
],
"type": "Organization"
},
"familyName": "Benezeth",
"givenName": "Yannick",
"id": "sg:person.016115557367.36",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016115557367.36"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Institut d\u2019Optique Graduate School",
"id": "https://www.grid.ac/institutes/grid.425181.b",
"name": [
"Institut d\u2019Optique Graduate School, 2 av. Augustin Fresnel, 91127, Palaiseau, France"
],
"type": "Organization"
},
"familyName": "Boffety",
"givenName": "Matthieu",
"id": "sg:person.01155755143.41",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01155755143.41"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Versailles Saint-Quentin-en-Yvelines University",
"id": "https://www.grid.ac/institutes/grid.12832.3a",
"name": [
"Service d\u2019anatomie et pathologie, H\u00f4pital Ambroise Par\u00e9, 9 av. Charles de Gaulle, 92104, Boulogne-Billancourt, France",
"Universit\u00e9 de Versailles-Saint-Quentin-en-Yvelines, 55 Avenue de Paris, 78000, Versailles, France"
],
"type": "Organization"
},
"familyName": "Emile",
"givenName": "Jean-Fran\u00e7ois",
"id": "sg:person.0610133233.71",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610133233.71"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Burgundy",
"id": "https://www.grid.ac/institutes/grid.5613.1",
"name": [
"Le2i - UMR CNRS 6306, Universit\u00e9 de Bourgogne, 9 av. Alain Savary, 21078, Dijon, France"
],
"type": "Organization"
},
"familyName": "Marzani",
"givenName": "Franck",
"id": "sg:person.07657000121.61",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07657000121.61"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Versailles Saint-Quentin-en-Yvelines University",
"id": "https://www.grid.ac/institutes/grid.12832.3a",
"name": [
"Service de Gastroendoscopy, H\u00f4pital Ambroise Par\u00e9, 9 av. Charles de Gaulle, 92104, Boulogne-Billancourt, France",
"Universit\u00e9 de Versailles-Saint-Quentin-en-Yvelines, 55 Avenue de Paris, 78000, Versailles, France"
],
"type": "Organization"
},
"familyName": "Lamarque",
"givenName": "Dominique",
"id": "sg:person.01060752353.05",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01060752353.05"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Institut d\u2019Optique Graduate School",
"id": "https://www.grid.ac/institutes/grid.425181.b",
"name": [
"Institut d\u2019Optique Graduate School, 2 av. Augustin Fresnel, 91127, Palaiseau, France"
],
"type": "Organization"
},
"familyName": "Goudail",
"givenName": "Fran\u00e7ois",
"id": "sg:person.0662564376.25",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0662564376.25"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/3-540-45014-9_1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000204280",
"https://doi.org/10.1007/3-540-45014-9_1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/3-540-45014-9_1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000204280",
"https://doi.org/10.1007/3-540-45014-9_1"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1117/1.jbo.18.2.026010",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001562796"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/b978-0-12-336156-1.50061-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003803373"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5772/52655",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004418224"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1162/neco.2009.10-08-892",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009343907"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.patrec.2012.07.020",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015412183"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.patcog.2013.03.007",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018031667"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1751-2980.2011.00550.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018505625"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.neunet.2013.11.009",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018609401"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0893-6080(02)00079-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022193802"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/9781444397772.ch14",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023678172"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.compmedimag.2010.07.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024693071"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5772/22689",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025150746"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/16.10.906",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029326163"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/3-540-55426-2_27",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036175401",
"https://doi.org/10.1007/3-540-55426-2_27"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10439-006-9121-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036757176",
"https://doi.org/10.1007/s10439-006-9121-9"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10439-006-9121-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036757176",
"https://doi.org/10.1007/s10439-006-9121-9"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1117/1.1766301",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037195886"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1117/12.816917",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037475916"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.gie.2008.01.013",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039788884"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1117/2.1200906.1560",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051253574"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1055/s-2003-40228",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1057422651"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1088/0031-9155/50/8/004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1059025965"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/34.574797",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061156543"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tgrs.2008.2011280",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061610700"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1214/12-aos1063",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1064393182"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1366/0003702981944571",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1065265187"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1366/0003702981944571",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1065265187"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/sitis.2012.125",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1094038965"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/cbms.2013.6627778",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1094204223"
],
"type": "CreativeWork"
}
],
"datePublished": "2016-03",
"datePublishedReg": "2016-03-01",
"description": "Gastric cancer is one of the fifth most deadly cancers worldwide. Nowadays the diagnosis is performed through gastroendoscopy under white light and histological analysis. However, the precancerous lesions are multifocalized and present low differences with respect to healthy tissue. Several systems have been proposed based on light tissue interaction to improve the visualization of malignancies. However, these systems are limited to few wavelengths. In this paper, we propose a minimally invasive technique based on multispectral imaging and a methodology to identify malignancies in the stomach. We developed a multispectral gastroendoscopic system compatible with current gastroendoscopes, where only the illumination is changed. The spectra are extracted from the acquired multispectral images in order to compute statistical features that are used to classify the data in two classes: healthy and malignant. The features are ranked by pooled variance t test to train three classifiers. Neural networks using generalized relevance learning vector quantization, support vector machine (SVM) with a Gaussian kernel and k-nn are evaluated using leave one patient out cross-validation. Taking into consideration the data collected in this work, the quantitative results from the classification using SVM show high accuracy and sensitivity using a low number of features. These results show the ability to discriminate malignancies of the gastric tissue. Therefore, multispectral imaging could help in the identification of malignancies during gastroendoscopy.",
"genre": "research_article",
"id": "sg:pub.10.1007/s11760-015-0779-z",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1050964",
"issn": [
"1863-1703",
"1863-1711"
],
"name": "Signal, Image and Video Processing",
"type": "Periodical"
},
{
"issueNumber": "3",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "10"
}
],
"name": "Identification of precancerous lesions by multispectral gastroendoscopy",
"pagination": "455-462",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"04e44a3860cbb444232379f0026fa0ee9180f88763b2760dba73ca8912fb7501"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s11760-015-0779-z"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1001026587"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s11760-015-0779-z",
"https://app.dimensions.ai/details/publication/pub.1001026587"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T02:06",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8700_00000519.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007%2Fs11760-015-0779-z"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11760-015-0779-z'
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/s11760-015-0779-z'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11760-015-0779-z'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11760-015-0779-z'
This table displays all metadata directly associated to this object as RDF triples.
199 TRIPLES
21 PREDICATES
55 URIs
19 LITERALS
7 BLANK NODES