Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study
Purpose. In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods. PET-CT images were collected from 22 newly diagnosed HNC patients,...
Gespeichert in:
| Veröffentlicht in: | Contrast media and molecular imaging 2018-01, Vol.2018 (2018), p.1-12 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 12 |
|---|---|
| container_issue | 2018 |
| container_start_page | 1 |
| container_title | Contrast media and molecular imaging |
| container_volume | 2018 |
| creator | Li, Qiaoliang Huang, Bingsheng Wang, Tian-Fu Zheng, Liyun Wong, Ching-Yee Oliver Feng, Shi-Ting Ye, Yufeng Wu, Po-Man Chen, Zhewei Huang, Bin Liu, Yong |
| description | Purpose. In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods. PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results. A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P |
| doi_str_mv | 10.1155/2018/8923028 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6220410</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2190115369</sourcerecordid><originalsourceid>FETCH-LOGICAL-c537t-ced95addd6ffbc4f99de1fb5081cae21a31f3dacbcbf803d738875f86ac0822d3</originalsourceid><addsrcrecordid>eNqNkctv1DAQhyMEoqVw44wscUGCUD_ycDggrdIX0gqQ2HK1Jva4TUnsxYlBe-B_x6tdlseJk8f2559m_GXZU0ZfM1aWp5wyeSobLiiX97LjdFTmhWD1_UNNm6Ps0TTdUVoUohEPsyNBi1pURXGc_biIw7Ahizj7EWY05AyH3iHMvXfEW3IZ_DSRVRx9IJ_9EEckNpVXCIaAM-Q96i-kBacxkPTi4_kqb1fkeurdTYrCNVkiBJd2b8iCnEUY8hbdnOBPczSbx9kDC8OET_brSXZ9cb5qr_Llh8t37WKZ61LUc67RNCUYYyprO13YpjHIbFdSyTQgZyCYFQZ0pzsrqTC1kLIuraxAU8m5ESfZ213uOnYjGp1aCDCodehHCBvloVd_37j-Vt34b6rinBaMpoAX-4Dgv0acZjX2k8ZhAIc-ToozIWlRCS4T-vwf9M7H4NJ4iWpociaqJlGvdpTefnBAe2iGUbX1qrZe1d5rwp_9OcAB_iUyAS93wG3vDHzv_zMOE4MWftNMsIo24icVw7Th</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2190115369</pqid></control><display><type>article</type><title>Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study</title><source>MEDLINE</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central Open Access</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><creator>Li, Qiaoliang ; Huang, Bingsheng ; Wang, Tian-Fu ; Zheng, Liyun ; Wong, Ching-Yee Oliver ; Feng, Shi-Ting ; Ye, Yufeng ; Wu, Po-Man ; Chen, Zhewei ; Huang, Bin ; Liu, Yong</creator><contributor>Pascali, Giancarlo ; Giancarlo Pascali</contributor><creatorcontrib>Li, Qiaoliang ; Huang, Bingsheng ; Wang, Tian-Fu ; Zheng, Liyun ; Wong, Ching-Yee Oliver ; Feng, Shi-Ting ; Ye, Yufeng ; Wu, Po-Man ; Chen, Zhewei ; Huang, Bin ; Liu, Yong ; Pascali, Giancarlo ; Giancarlo Pascali</creatorcontrib><description>Purpose. In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods. PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results. A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P<0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion. A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.</description><identifier>ISSN: 1555-4309</identifier><identifier>EISSN: 1555-4317</identifier><identifier>DOI: 10.1155/2018/8923028</identifier><identifier>PMID: 30473644</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Artificial neural networks ; Automation ; Brain cancer ; Cancer ; Cancer therapies ; Computed tomography ; Contouring ; Correlation analysis ; Deep Learning ; Female ; Head & neck cancer ; Head and Neck Neoplasms - diagnostic imaging ; Humans ; Image processing ; Image segmentation ; International conferences ; Male ; Medical imaging ; Methods ; Middle Aged ; Models, Theoretical ; Neural networks ; NMR ; Nuclear magnetic resonance ; Positron emission ; Positron emission tomography ; Positron Emission Tomography Computed Tomography ; Semantics ; Tomography ; Tumors</subject><ispartof>Contrast media and molecular imaging, 2018-01, Vol.2018 (2018), p.1-12</ispartof><rights>Copyright © 2018 Bin Huang et al.</rights><rights>Copyright © 2018 Bin Huang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2018 Bin Huang et al. 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c537t-ced95addd6ffbc4f99de1fb5081cae21a31f3dacbcbf803d738875f86ac0822d3</citedby><cites>FETCH-LOGICAL-c537t-ced95addd6ffbc4f99de1fb5081cae21a31f3dacbcbf803d738875f86ac0822d3</cites><orcidid>0000-0002-1183-7506 ; 0000-0003-2876-4152 ; 0000-0002-9843-475X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6220410/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6220410/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,882,27905,27906,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30473644$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Pascali, Giancarlo</contributor><contributor>Giancarlo Pascali</contributor><creatorcontrib>Li, Qiaoliang</creatorcontrib><creatorcontrib>Huang, Bingsheng</creatorcontrib><creatorcontrib>Wang, Tian-Fu</creatorcontrib><creatorcontrib>Zheng, Liyun</creatorcontrib><creatorcontrib>Wong, Ching-Yee Oliver</creatorcontrib><creatorcontrib>Feng, Shi-Ting</creatorcontrib><creatorcontrib>Ye, Yufeng</creatorcontrib><creatorcontrib>Wu, Po-Man</creatorcontrib><creatorcontrib>Chen, Zhewei</creatorcontrib><creatorcontrib>Huang, Bin</creatorcontrib><creatorcontrib>Liu, Yong</creatorcontrib><title>Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study</title><title>Contrast media and molecular imaging</title><addtitle>Contrast Media Mol Imaging</addtitle><description>Purpose. In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods. PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results. A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P<0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion. A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.</description><subject>Accuracy</subject><subject>Artificial neural networks</subject><subject>Automation</subject><subject>Brain cancer</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Computed tomography</subject><subject>Contouring</subject><subject>Correlation analysis</subject><subject>Deep Learning</subject><subject>Female</subject><subject>Head & neck cancer</subject><subject>Head and Neck Neoplasms - diagnostic imaging</subject><subject>Humans</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>International conferences</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Models, Theoretical</subject><subject>Neural networks</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Positron emission</subject><subject>Positron emission tomography</subject><subject>Positron Emission Tomography Computed Tomography</subject><subject>Semantics</subject><subject>Tomography</subject><subject>Tumors</subject><issn>1555-4309</issn><issn>1555-4317</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNqNkctv1DAQhyMEoqVw44wscUGCUD_ycDggrdIX0gqQ2HK1Jva4TUnsxYlBe-B_x6tdlseJk8f2559m_GXZU0ZfM1aWp5wyeSobLiiX97LjdFTmhWD1_UNNm6Ps0TTdUVoUohEPsyNBi1pURXGc_biIw7Ahizj7EWY05AyH3iHMvXfEW3IZ_DSRVRx9IJ_9EEckNpVXCIaAM-Q96i-kBacxkPTi4_kqb1fkeurdTYrCNVkiBJd2b8iCnEUY8hbdnOBPczSbx9kDC8OET_brSXZ9cb5qr_Llh8t37WKZ61LUc67RNCUYYyprO13YpjHIbFdSyTQgZyCYFQZ0pzsrqTC1kLIuraxAU8m5ESfZ213uOnYjGp1aCDCodehHCBvloVd_37j-Vt34b6rinBaMpoAX-4Dgv0acZjX2k8ZhAIc-ToozIWlRCS4T-vwf9M7H4NJ4iWpociaqJlGvdpTefnBAe2iGUbX1qrZe1d5rwp_9OcAB_iUyAS93wG3vDHzv_zMOE4MWftNMsIo24icVw7Th</recordid><startdate>20180101</startdate><enddate>20180101</enddate><creator>Li, Qiaoliang</creator><creator>Huang, Bingsheng</creator><creator>Wang, Tian-Fu</creator><creator>Zheng, Liyun</creator><creator>Wong, Ching-Yee Oliver</creator><creator>Feng, Shi-Ting</creator><creator>Ye, Yufeng</creator><creator>Wu, Po-Man</creator><creator>Chen, Zhewei</creator><creator>Huang, Bin</creator><creator>Liu, Yong</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><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>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BYOGL</scope><scope>CCPQU</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1183-7506</orcidid><orcidid>https://orcid.org/0000-0003-2876-4152</orcidid><orcidid>https://orcid.org/0000-0002-9843-475X</orcidid></search><sort><creationdate>20180101</creationdate><title>Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study</title><author>Li, Qiaoliang ; Huang, Bingsheng ; Wang, Tian-Fu ; Zheng, Liyun ; Wong, Ching-Yee Oliver ; Feng, Shi-Ting ; Ye, Yufeng ; Wu, Po-Man ; Chen, Zhewei ; Huang, Bin ; Liu, Yong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c537t-ced95addd6ffbc4f99de1fb5081cae21a31f3dacbcbf803d738875f86ac0822d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Accuracy</topic><topic>Artificial neural networks</topic><topic>Automation</topic><topic>Brain cancer</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>Computed tomography</topic><topic>Contouring</topic><topic>Correlation analysis</topic><topic>Deep Learning</topic><topic>Female</topic><topic>Head & neck cancer</topic><topic>Head and Neck Neoplasms - diagnostic imaging</topic><topic>Humans</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>International conferences</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Models, Theoretical</topic><topic>Neural networks</topic><topic>NMR</topic><topic>Nuclear magnetic resonance</topic><topic>Positron emission</topic><topic>Positron emission tomography</topic><topic>Positron Emission Tomography Computed Tomography</topic><topic>Semantics</topic><topic>Tomography</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Qiaoliang</creatorcontrib><creatorcontrib>Huang, Bingsheng</creatorcontrib><creatorcontrib>Wang, Tian-Fu</creatorcontrib><creatorcontrib>Zheng, Liyun</creatorcontrib><creatorcontrib>Wong, Ching-Yee Oliver</creatorcontrib><creatorcontrib>Feng, Shi-Ting</creatorcontrib><creatorcontrib>Ye, Yufeng</creatorcontrib><creatorcontrib>Wu, Po-Man</creatorcontrib><creatorcontrib>Chen, Zhewei</creatorcontrib><creatorcontrib>Huang, Bin</creatorcontrib><creatorcontrib>Liu, Yong</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>East Europe, Central Europe Database</collection><collection>ProQuest One Community College</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Contrast media and molecular imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Qiaoliang</au><au>Huang, Bingsheng</au><au>Wang, Tian-Fu</au><au>Zheng, Liyun</au><au>Wong, Ching-Yee Oliver</au><au>Feng, Shi-Ting</au><au>Ye, Yufeng</au><au>Wu, Po-Man</au><au>Chen, Zhewei</au><au>Huang, Bin</au><au>Liu, Yong</au><au>Pascali, Giancarlo</au><au>Giancarlo Pascali</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study</atitle><jtitle>Contrast media and molecular imaging</jtitle><addtitle>Contrast Media Mol Imaging</addtitle><date>2018-01-01</date><risdate>2018</risdate><volume>2018</volume><issue>2018</issue><spage>1</spage><epage>12</epage><pages>1-12</pages><issn>1555-4309</issn><eissn>1555-4317</eissn><abstract>Purpose. In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods. PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results. A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P<0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion. A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><pmid>30473644</pmid><doi>10.1155/2018/8923028</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-1183-7506</orcidid><orcidid>https://orcid.org/0000-0003-2876-4152</orcidid><orcidid>https://orcid.org/0000-0002-9843-475X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1555-4309 |
| ispartof | Contrast media and molecular imaging, 2018-01, Vol.2018 (2018), p.1-12 |
| issn | 1555-4309 1555-4317 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6220410 |
| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; PubMed Central; Alma/SFX Local Collection |
| subjects | Accuracy Artificial neural networks Automation Brain cancer Cancer Cancer therapies Computed tomography Contouring Correlation analysis Deep Learning Female Head & neck cancer Head and Neck Neoplasms - diagnostic imaging Humans Image processing Image segmentation International conferences Male Medical imaging Methods Middle Aged Models, Theoretical Neural networks NMR Nuclear magnetic resonance Positron emission Positron emission tomography Positron Emission Tomography Computed Tomography Semantics Tomography Tumors |
| title | Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T23%3A59%3A22IST&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=Fully%20Automated%20Delineation%20of%20Gross%20Tumor%20Volume%20for%20Head%20and%20Neck%20Cancer%20on%20PET-CT%20Using%20Deep%20Learning:%20A%20Dual-Center%20Study&rft.jtitle=Contrast%20media%20and%20molecular%20imaging&rft.au=Li,%20Qiaoliang&rft.date=2018-01-01&rft.volume=2018&rft.issue=2018&rft.spage=1&rft.epage=12&rft.pages=1-12&rft.issn=1555-4309&rft.eissn=1555-4317&rft_id=info:doi/10.1155/2018/8923028&rft_dat=%3Cproquest_pubme%3E2190115369%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=2190115369&rft_id=info:pmid/30473644&rfr_iscdi=true |