Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging
Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural networ...
Gespeichert in:
| Veröffentlicht in: | Journal of biomedical optics 2017-06, Vol.22 (6), p.060503-060503 |
|---|---|
| Hauptverfasser: | , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 060503 |
|---|---|
| container_issue | 6 |
| container_start_page | 060503 |
| container_title | Journal of biomedical optics |
| container_volume | 22 |
| creator | Halicek, Martin Lu, Guolan Little, James V Wang, Xu Patel, Mihir Griffith, Christopher C El-Deiry, Mark W Chen, Amy Y Fei, Baowei |
| description | Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients. |
| doi_str_mv | 10.1117/1.JBO.22.6.060503 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5482930</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1914581283</sourcerecordid><originalsourceid>FETCH-LOGICAL-c677t-f11efaf2888d186988444e5065b85e452a664f3c800b79e066133b0489f9cd2e3</originalsourceid><addsrcrecordid>eNp9kUtP3DAUhS1UVCjtD2BTZdlNUj9ij7OpxJsiJLqga8vjXA-GjB3sBDT8-npmYERpxepa937n-EgHoX2CK0LI5DupLg6vKkorUWGBOWZbaJdwgUtKJfmQ31iykgkhd9CnlG4xxlI04iPaoVJwjjnfReYYoC9M8A-hGwcXvO4KD2NcjeExxLtU2BAL0-mUnF04PytuQLeF9m0mzF1htDcQizGtToseYurBDEsHN9ezvP2Mtq3uEnx5nnvo9-nJ9dF5eXl19vPo4LI0YjIZSksIWG2plLIlOaiUdV0Dx4JPJYeaUy1EbZmRGE8nDWAhCGNTXMvGNqalwPbQj7VvP07n0BrwyxSqjzlHXKignfr74t2NmoUHxWtJG4azwbdngxjuR0iDmrtkoOu0hzAmRRpSc0moZBkla9TEkFIEu_mGYLUsRxGVy1GUKqHW5WTN19f5NoqXNjJQrYHUO1C3YYy5jvSu4_X_BBvsyfVvNavdQRyc6eDX8ek_57617A_5v7ZQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1914581283</pqid></control><display><type>article</type><title>Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging</title><source>MEDLINE</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><creator>Halicek, Martin ; Lu, Guolan ; Little, James V ; Wang, Xu ; Patel, Mihir ; Griffith, Christopher C ; El-Deiry, Mark W ; Chen, Amy Y ; Fei, Baowei</creator><creatorcontrib>Halicek, Martin ; Lu, Guolan ; Little, James V ; Wang, Xu ; Patel, Mihir ; Griffith, Christopher C ; El-Deiry, Mark W ; Chen, Amy Y ; Fei, Baowei</creatorcontrib><description>Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients.</description><identifier>ISSN: 1083-3668</identifier><identifier>EISSN: 1560-2281</identifier><identifier>DOI: 10.1117/1.JBO.22.6.060503</identifier><identifier>PMID: 28655055</identifier><language>eng</language><publisher>United States: Society of Photo-Optical Instrumentation Engineers</publisher><subject>Diagnostic Imaging - methods ; Head and Neck Neoplasms - diagnostic imaging ; Humans ; JBO Letters ; Letter ; Neural Networks (Computer)</subject><ispartof>Journal of biomedical optics, 2017-06, Vol.22 (6), p.060503-060503</ispartof><rights>The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.</rights><rights>The Authors. The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c677t-f11efaf2888d186988444e5065b85e452a664f3c800b79e066133b0489f9cd2e3</citedby><cites>FETCH-LOGICAL-c677t-f11efaf2888d186988444e5065b85e452a664f3c800b79e066133b0489f9cd2e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5482930/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5482930/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28655055$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Halicek, Martin</creatorcontrib><creatorcontrib>Lu, Guolan</creatorcontrib><creatorcontrib>Little, James V</creatorcontrib><creatorcontrib>Wang, Xu</creatorcontrib><creatorcontrib>Patel, Mihir</creatorcontrib><creatorcontrib>Griffith, Christopher C</creatorcontrib><creatorcontrib>El-Deiry, Mark W</creatorcontrib><creatorcontrib>Chen, Amy Y</creatorcontrib><creatorcontrib>Fei, Baowei</creatorcontrib><title>Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging</title><title>Journal of biomedical optics</title><addtitle>J. Biomed. Opt</addtitle><description>Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients.</description><subject>Diagnostic Imaging - methods</subject><subject>Head and Neck Neoplasms - diagnostic imaging</subject><subject>Humans</subject><subject>JBO Letters</subject><subject>Letter</subject><subject>Neural Networks (Computer)</subject><issn>1083-3668</issn><issn>1560-2281</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kUtP3DAUhS1UVCjtD2BTZdlNUj9ij7OpxJsiJLqga8vjXA-GjB3sBDT8-npmYERpxepa937n-EgHoX2CK0LI5DupLg6vKkorUWGBOWZbaJdwgUtKJfmQ31iykgkhd9CnlG4xxlI04iPaoVJwjjnfReYYoC9M8A-hGwcXvO4KD2NcjeExxLtU2BAL0-mUnF04PytuQLeF9m0mzF1htDcQizGtToseYurBDEsHN9ezvP2Mtq3uEnx5nnvo9-nJ9dF5eXl19vPo4LI0YjIZSksIWG2plLIlOaiUdV0Dx4JPJYeaUy1EbZmRGE8nDWAhCGNTXMvGNqalwPbQj7VvP07n0BrwyxSqjzlHXKignfr74t2NmoUHxWtJG4azwbdngxjuR0iDmrtkoOu0hzAmRRpSc0moZBkla9TEkFIEu_mGYLUsRxGVy1GUKqHW5WTN19f5NoqXNjJQrYHUO1C3YYy5jvSu4_X_BBvsyfVvNavdQRyc6eDX8ek_57617A_5v7ZQ</recordid><startdate>20170601</startdate><enddate>20170601</enddate><creator>Halicek, Martin</creator><creator>Lu, Guolan</creator><creator>Little, James V</creator><creator>Wang, Xu</creator><creator>Patel, Mihir</creator><creator>Griffith, Christopher C</creator><creator>El-Deiry, Mark W</creator><creator>Chen, Amy Y</creator><creator>Fei, Baowei</creator><general>Society of Photo-Optical Instrumentation Engineers</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20170601</creationdate><title>Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging</title><author>Halicek, Martin ; Lu, Guolan ; Little, James V ; Wang, Xu ; Patel, Mihir ; Griffith, Christopher C ; El-Deiry, Mark W ; Chen, Amy Y ; Fei, Baowei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c677t-f11efaf2888d186988444e5065b85e452a664f3c800b79e066133b0489f9cd2e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Diagnostic Imaging - methods</topic><topic>Head and Neck Neoplasms - diagnostic imaging</topic><topic>Humans</topic><topic>JBO Letters</topic><topic>Letter</topic><topic>Neural Networks (Computer)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Halicek, Martin</creatorcontrib><creatorcontrib>Lu, Guolan</creatorcontrib><creatorcontrib>Little, James V</creatorcontrib><creatorcontrib>Wang, Xu</creatorcontrib><creatorcontrib>Patel, Mihir</creatorcontrib><creatorcontrib>Griffith, Christopher C</creatorcontrib><creatorcontrib>El-Deiry, Mark W</creatorcontrib><creatorcontrib>Chen, Amy Y</creatorcontrib><creatorcontrib>Fei, Baowei</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of biomedical optics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Halicek, Martin</au><au>Lu, Guolan</au><au>Little, James V</au><au>Wang, Xu</au><au>Patel, Mihir</au><au>Griffith, Christopher C</au><au>El-Deiry, Mark W</au><au>Chen, Amy Y</au><au>Fei, Baowei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging</atitle><jtitle>Journal of biomedical optics</jtitle><addtitle>J. Biomed. Opt</addtitle><date>2017-06-01</date><risdate>2017</risdate><volume>22</volume><issue>6</issue><spage>060503</spage><epage>060503</epage><pages>060503-060503</pages><issn>1083-3668</issn><eissn>1560-2281</eissn><abstract>Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients.</abstract><cop>United States</cop><pub>Society of Photo-Optical Instrumentation Engineers</pub><pmid>28655055</pmid><doi>10.1117/1.JBO.22.6.060503</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1083-3668 |
| ispartof | Journal of biomedical optics, 2017-06, Vol.22 (6), p.060503-060503 |
| issn | 1083-3668 1560-2281 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5482930 |
| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central |
| subjects | Diagnostic Imaging - methods Head and Neck Neoplasms - diagnostic imaging Humans JBO Letters Letter Neural Networks (Computer) |
| title | Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T21%3A06%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Deep%20convolutional%20neural%20networks%20for%20classifying%20head%20and%20neck%20cancer%20using%20hyperspectral%20imaging&rft.jtitle=Journal%20of%20biomedical%20optics&rft.au=Halicek,%20Martin&rft.date=2017-06-01&rft.volume=22&rft.issue=6&rft.spage=060503&rft.epage=060503&rft.pages=060503-060503&rft.issn=1083-3668&rft.eissn=1560-2281&rft_id=info:doi/10.1117/1.JBO.22.6.060503&rft_dat=%3Cproquest_pubme%3E1914581283%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1914581283&rft_id=info:pmid/28655055&rfr_iscdi=true |