Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis
Objectives Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to prov...
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| Veröffentlicht in: | European radiology 2021-12, Vol.31 (12), p.9638-9653 |
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| creator | van Kempen, Evi J. Post, Max Mannil, Manoj Witkam, Richard L. ter Laan, Mark Patel, Ajay Meijer, Frederick J. A. Henssen, Dylan |
| description | Objectives
Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods.
Methods
A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033).
Results
After the literature search (
n
= 734), 42 studies were included in the systematic literature review. Ten studies were eligible for inclusion in the meta-analysis. Overall, the MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86). In addition, a DSC score of 0.83 (95% CI: 0.80–0.87) and 0.82 (95% CI: 0.78–0.87) was observed for the automated glioma segmentation of the high-grade and low-grade gliomas, respectively. However, heterogeneity was considerably high between included studies, and publication bias was observed.
Conclusion
MLAs facilitating automated segmentation of gliomas show good accuracy, which is promising for future implementation in neuroradiology. However, before actual implementation, a few hurdles are yet to be overcome. It is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.
Key Points
• MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86), indicating a good performance.
• MLA performance was comparable when comparing the segmentation results of the high-grade gliomas and the low-grade gliomas.
• For future studies using MLAs, it is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set. |
| doi_str_mv | 10.1007/s00330-021-08035-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8589805</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2596810617</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-6deca905afb1230af7537bc53ddc30db3da6f7ea2c0e3a970dd7d2ff868668373</originalsourceid><addsrcrecordid>eNp9kU9v1DAUxC0EotvCF-CALHHhEniOk9jhgFRVFCoVgRCcrRfnJesqsYudFO2Rb46XLeXPgZMP85vxPA1jTwS8EADqZQKQEgooRQEaZF3APbYRlSwLAbq6zzbQSl2otq2O2HFKVwDQiko9ZEeyAtGKUm_Y948UhxBn9JZ4GPiMdus88YkweudHjtMYolu2c-KZ4-Pkwow80TiTX3Bxwe9tXUTn-ftPF694FndpoTlrlk9uoYjLGolHunH0jaPv-UwLFuhx2iWXHrEHA06JHt--J-zL-ZvPZ--Kyw9vL85OLwtbqWopmp4stlDj0IlSAg6qlqqztex7K6HvZI_NoAhLCySxVdD3qi-HQTe6abRU8oS9PuRer91Mvc31I07mOroZ484EdOZvxbutGcON0bVuNdQ54PltQAxfV0qLmV2yNE3oKazJlLUUpVCy0hl99g96FdaYD95TbaMFNGLfqDxQNoaUIg13ZQSY_cLmsLDJC5ufCxvIpqd_nnFn-TVpBuQBSFnyI8Xff_8n9gf7nbTI</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2596810617</pqid></control><display><type>article</type><title>Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>van Kempen, Evi J. ; Post, Max ; Mannil, Manoj ; Witkam, Richard L. ; ter Laan, Mark ; Patel, Ajay ; Meijer, Frederick J. A. ; Henssen, Dylan</creator><creatorcontrib>van Kempen, Evi J. ; Post, Max ; Mannil, Manoj ; Witkam, Richard L. ; ter Laan, Mark ; Patel, Ajay ; Meijer, Frederick J. A. ; Henssen, Dylan</creatorcontrib><description>Objectives
Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods.
Methods
A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033).
Results
After the literature search (
n
= 734), 42 studies were included in the systematic literature review. Ten studies were eligible for inclusion in the meta-analysis. Overall, the MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86). In addition, a DSC score of 0.83 (95% CI: 0.80–0.87) and 0.82 (95% CI: 0.78–0.87) was observed for the automated glioma segmentation of the high-grade and low-grade gliomas, respectively. However, heterogeneity was considerably high between included studies, and publication bias was observed.
Conclusion
MLAs facilitating automated segmentation of gliomas show good accuracy, which is promising for future implementation in neuroradiology. However, before actual implementation, a few hurdles are yet to be overcome. It is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.
Key Points
• MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86), indicating a good performance.
• MLA performance was comparable when comparing the segmentation results of the high-grade gliomas and the low-grade gliomas.
• For future studies using MLAs, it is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.</description><identifier>ISSN: 0938-7994</identifier><identifier>ISSN: 1432-1084</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-021-08035-0</identifier><identifier>PMID: 34019128</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Automation ; Brain - diagnostic imaging ; Brain Neoplasms - diagnostic imaging ; Brain tumors ; Diagnostic Radiology ; Glioma ; Glioma - diagnostic imaging ; Guidelines ; Heterogeneity ; Humans ; Image processing ; Image segmentation ; Imaging ; Imaging Informatics and Artificial Intelligence ; Internal Medicine ; Interventional Radiology ; Learning algorithms ; Literature reviews ; Machine Learning ; Magnetic Resonance Imaging ; Medicine ; Medicine & Public Health ; Meta-analysis ; Neuroimaging ; Neuroradiology ; Radiology ; Subgroups ; Systematic review ; Test sets ; Ultrasound</subject><ispartof>European radiology, 2021-12, Vol.31 (12), p.9638-9653</ispartof><rights>The Author(s) 2021</rights><rights>2021. The Author(s).</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-6deca905afb1230af7537bc53ddc30db3da6f7ea2c0e3a970dd7d2ff868668373</citedby><cites>FETCH-LOGICAL-c474t-6deca905afb1230af7537bc53ddc30db3da6f7ea2c0e3a970dd7d2ff868668373</cites><orcidid>0000-0002-3915-3034</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-021-08035-0$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-021-08035-0$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34019128$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>van Kempen, Evi J.</creatorcontrib><creatorcontrib>Post, Max</creatorcontrib><creatorcontrib>Mannil, Manoj</creatorcontrib><creatorcontrib>Witkam, Richard L.</creatorcontrib><creatorcontrib>ter Laan, Mark</creatorcontrib><creatorcontrib>Patel, Ajay</creatorcontrib><creatorcontrib>Meijer, Frederick J. A.</creatorcontrib><creatorcontrib>Henssen, Dylan</creatorcontrib><title>Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods.
Methods
A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033).
Results
After the literature search (
n
= 734), 42 studies were included in the systematic literature review. Ten studies were eligible for inclusion in the meta-analysis. Overall, the MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86). In addition, a DSC score of 0.83 (95% CI: 0.80–0.87) and 0.82 (95% CI: 0.78–0.87) was observed for the automated glioma segmentation of the high-grade and low-grade gliomas, respectively. However, heterogeneity was considerably high between included studies, and publication bias was observed.
Conclusion
MLAs facilitating automated segmentation of gliomas show good accuracy, which is promising for future implementation in neuroradiology. However, before actual implementation, a few hurdles are yet to be overcome. It is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.
Key Points
• MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86), indicating a good performance.
• MLA performance was comparable when comparing the segmentation results of the high-grade gliomas and the low-grade gliomas.
• For future studies using MLAs, it is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.</description><subject>Algorithms</subject><subject>Automation</subject><subject>Brain - diagnostic imaging</subject><subject>Brain Neoplasms - diagnostic imaging</subject><subject>Brain tumors</subject><subject>Diagnostic Radiology</subject><subject>Glioma</subject><subject>Glioma - diagnostic imaging</subject><subject>Guidelines</subject><subject>Heterogeneity</subject><subject>Humans</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Imaging</subject><subject>Imaging Informatics and Artificial Intelligence</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Learning algorithms</subject><subject>Literature reviews</subject><subject>Machine Learning</subject><subject>Magnetic Resonance Imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Meta-analysis</subject><subject>Neuroimaging</subject><subject>Neuroradiology</subject><subject>Radiology</subject><subject>Subgroups</subject><subject>Systematic review</subject><subject>Test sets</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kU9v1DAUxC0EotvCF-CALHHhEniOk9jhgFRVFCoVgRCcrRfnJesqsYudFO2Rb46XLeXPgZMP85vxPA1jTwS8EADqZQKQEgooRQEaZF3APbYRlSwLAbq6zzbQSl2otq2O2HFKVwDQiko9ZEeyAtGKUm_Y948UhxBn9JZ4GPiMdus88YkweudHjtMYolu2c-KZ4-Pkwow80TiTX3Bxwe9tXUTn-ftPF694FndpoTlrlk9uoYjLGolHunH0jaPv-UwLFuhx2iWXHrEHA06JHt--J-zL-ZvPZ--Kyw9vL85OLwtbqWopmp4stlDj0IlSAg6qlqqztex7K6HvZI_NoAhLCySxVdD3qi-HQTe6abRU8oS9PuRer91Mvc31I07mOroZ484EdOZvxbutGcON0bVuNdQ54PltQAxfV0qLmV2yNE3oKazJlLUUpVCy0hl99g96FdaYD95TbaMFNGLfqDxQNoaUIg13ZQSY_cLmsLDJC5ufCxvIpqd_nnFn-TVpBuQBSFnyI8Xff_8n9gf7nbTI</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>van Kempen, Evi J.</creator><creator>Post, Max</creator><creator>Mannil, Manoj</creator><creator>Witkam, Richard L.</creator><creator>ter Laan, Mark</creator><creator>Patel, Ajay</creator><creator>Meijer, Frederick J. A.</creator><creator>Henssen, Dylan</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>C6C</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>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</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/0000-0002-3915-3034</orcidid></search><sort><creationdate>20211201</creationdate><title>Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis</title><author>van Kempen, Evi J. ; Post, Max ; Mannil, Manoj ; Witkam, Richard L. ; ter Laan, Mark ; Patel, Ajay ; Meijer, Frederick J. A. ; Henssen, Dylan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-6deca905afb1230af7537bc53ddc30db3da6f7ea2c0e3a970dd7d2ff868668373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Automation</topic><topic>Brain - diagnostic imaging</topic><topic>Brain Neoplasms - diagnostic imaging</topic><topic>Brain tumors</topic><topic>Diagnostic Radiology</topic><topic>Glioma</topic><topic>Glioma - diagnostic imaging</topic><topic>Guidelines</topic><topic>Heterogeneity</topic><topic>Humans</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Imaging</topic><topic>Imaging Informatics and Artificial Intelligence</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Learning algorithms</topic><topic>Literature reviews</topic><topic>Machine Learning</topic><topic>Magnetic Resonance Imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Meta-analysis</topic><topic>Neuroimaging</topic><topic>Neuroradiology</topic><topic>Radiology</topic><topic>Subgroups</topic><topic>Systematic review</topic><topic>Test sets</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>van Kempen, Evi J.</creatorcontrib><creatorcontrib>Post, Max</creatorcontrib><creatorcontrib>Mannil, Manoj</creatorcontrib><creatorcontrib>Witkam, Richard L.</creatorcontrib><creatorcontrib>ter Laan, Mark</creatorcontrib><creatorcontrib>Patel, Ajay</creatorcontrib><creatorcontrib>Meijer, Frederick J. A.</creatorcontrib><creatorcontrib>Henssen, Dylan</creatorcontrib><collection>Springer Nature OA Free Journals</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>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</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>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</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>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>van Kempen, Evi J.</au><au>Post, Max</au><au>Mannil, Manoj</au><au>Witkam, Richard L.</au><au>ter Laan, Mark</au><au>Patel, Ajay</au><au>Meijer, Frederick J. A.</au><au>Henssen, Dylan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2021-12-01</date><risdate>2021</risdate><volume>31</volume><issue>12</issue><spage>9638</spage><epage>9653</epage><pages>9638-9653</pages><issn>0938-7994</issn><issn>1432-1084</issn><eissn>1432-1084</eissn><abstract>Objectives
Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods.
Methods
A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033).
Results
After the literature search (
n
= 734), 42 studies were included in the systematic literature review. Ten studies were eligible for inclusion in the meta-analysis. Overall, the MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86). In addition, a DSC score of 0.83 (95% CI: 0.80–0.87) and 0.82 (95% CI: 0.78–0.87) was observed for the automated glioma segmentation of the high-grade and low-grade gliomas, respectively. However, heterogeneity was considerably high between included studies, and publication bias was observed.
Conclusion
MLAs facilitating automated segmentation of gliomas show good accuracy, which is promising for future implementation in neuroradiology. However, before actual implementation, a few hurdles are yet to be overcome. It is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.
Key Points
• MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86), indicating a good performance.
• MLA performance was comparable when comparing the segmentation results of the high-grade gliomas and the low-grade gliomas.
• For future studies using MLAs, it is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>34019128</pmid><doi>10.1007/s00330-021-08035-0</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-3915-3034</orcidid><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Automation Brain - diagnostic imaging Brain Neoplasms - diagnostic imaging Brain tumors Diagnostic Radiology Glioma Glioma - diagnostic imaging Guidelines Heterogeneity Humans Image processing Image segmentation Imaging Imaging Informatics and Artificial Intelligence Internal Medicine Interventional Radiology Learning algorithms Literature reviews Machine Learning Magnetic Resonance Imaging Medicine Medicine & Public Health Meta-analysis Neuroimaging Neuroradiology Radiology Subgroups Systematic review Test sets Ultrasound |
| title | Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis |
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