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MRI‐based thalamic volumetry in multiple sclerosis using FSL‐FIRST: Systematic assessment of common error modes
Background and Purpose FSL's FMRIB's Integrated Registration and Segmentation Tool (FSL‐FIRST) is a widely used and well‐validated tool. Automated thalamic segmentation is a common application and an important longitudinal measure for multiple sclerosis (MS). However, FSL‐FIRST's algo...
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| Published in: | Journal of neuroimaging 2022-03, Vol.32 (2), p.245-252 |
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| creator | Lyman, Cassondra Lee, Dongchan Ferrari, Hannah Fuchs, Tom A. Bergsland, Niels Jakimovski, Dejan Weinstock‐Guttmann, Bianca Zivadinov, Robert Dwyer, Michael G. |
| description | Background and Purpose
FSL's FMRIB's Integrated Registration and Segmentation Tool (FSL‐FIRST) is a widely used and well‐validated tool. Automated thalamic segmentation is a common application and an important longitudinal measure for multiple sclerosis (MS). However, FSL‐FIRST's algorithm is based on shape models derived from non‐MS groups. As such, the present study sought to systematically assess common thalamic segmentation errors made by FSL‐FIRST on MRIs from people with multiple sclerosis (PwMS).
Methods
FSL‐FIRST was applied to generate thalamic segmentation masks for 890 MR images in PwMS. Images and masks were reviewed systematically to classify and quantify errors, as well as associated anatomical variations and MRI abnormalities. For cases with overt errors (n = 362), thalamic masks were corrected and quantitative volumetric differences were calculated.
Results
In the entire quantitative volumetric group, the mean volumetric error of FSL‐FIRST was 2.74% (0.360 ml): among only corrected cases, the mean volumetric error was 6.79% (0.894 ml). The average percent volumetric error associated with seven error types, two anatomical variants, and motions artifacts are reported. Additional analyses showed that the presence of motion artifacts or anatomical variations significantly increased the probability of error (χ2 = 18.14, p |
| doi_str_mv | 10.1111/jon.12947 |
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| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2597492479</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2637282293</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3537-355bf53a88f8f221c18309f914ffed4703f0c403faea9aba5ce1d89f8108fb4c3</originalsourceid><addsrcrecordid>eNp1kc9u1DAQxi0EoqVw4AWQJS5wSBv_S2xuqGJh0UKlbjlHjjOGrOx48SSgvfEIPCNPgmELByTmMDOH3_dp9A0hj1l9zkpd7NJ0zriR7R1yypTiVaMac7fstWIV51qekAeIu7rmTHJxn5wI2TZto-UpwXfX6x_fvvcWYaDzJxtsHB39ksISYc4HOk40LmEe9wEougA54Yh0wXH6SFfbTZGu1tfbmxd0e8AZop2L2iICYoRppslTl2JME4WcU6YxDYAPyT1vA8Kj23lGPqxe3Vy-qTZXr9eXLzeVE0q0lVCq90pYrb32nDPHtKiNN0x6D4Nsa-FrJ0u3YI3trXLABm28ZrX2vXTijDw7-u5z-rwAzl0c0UEIdoK0YMeVaaXhsjUFffoPuktLnsp1HW9EyzXnRhTq-ZFyJQbM4Lt9HqPNh47V3a9PFNXU_f5EYZ_cOi59hOEv-Sf6Alwcga9jgMP_nbq3V--Plj8BaXiVNg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2637282293</pqid></control><display><type>article</type><title>MRI‐based thalamic volumetry in multiple sclerosis using FSL‐FIRST: Systematic assessment of common error modes</title><source>Wiley</source><creator>Lyman, Cassondra ; Lee, Dongchan ; Ferrari, Hannah ; Fuchs, Tom A. ; Bergsland, Niels ; Jakimovski, Dejan ; Weinstock‐Guttmann, Bianca ; Zivadinov, Robert ; Dwyer, Michael G.</creator><creatorcontrib>Lyman, Cassondra ; Lee, Dongchan ; Ferrari, Hannah ; Fuchs, Tom A. ; Bergsland, Niels ; Jakimovski, Dejan ; Weinstock‐Guttmann, Bianca ; Zivadinov, Robert ; Dwyer, Michael G.</creatorcontrib><description>Background and Purpose
FSL's FMRIB's Integrated Registration and Segmentation Tool (FSL‐FIRST) is a widely used and well‐validated tool. Automated thalamic segmentation is a common application and an important longitudinal measure for multiple sclerosis (MS). However, FSL‐FIRST's algorithm is based on shape models derived from non‐MS groups. As such, the present study sought to systematically assess common thalamic segmentation errors made by FSL‐FIRST on MRIs from people with multiple sclerosis (PwMS).
Methods
FSL‐FIRST was applied to generate thalamic segmentation masks for 890 MR images in PwMS. Images and masks were reviewed systematically to classify and quantify errors, as well as associated anatomical variations and MRI abnormalities. For cases with overt errors (n = 362), thalamic masks were corrected and quantitative volumetric differences were calculated.
Results
In the entire quantitative volumetric group, the mean volumetric error of FSL‐FIRST was 2.74% (0.360 ml): among only corrected cases, the mean volumetric error was 6.79% (0.894 ml). The average percent volumetric error associated with seven error types, two anatomical variants, and motions artifacts are reported. Additional analyses showed that the presence of motion artifacts or anatomical variations significantly increased the probability of error (χ2 = 18.14, p < .01 and χ2 = 64.89, p < .001, respectively). Finally, thalamus volume error was negatively associated with degree of atrophy, such that smaller thalami were systematically overestimated (r = –.28, p < .001).
Conclusions
In PwMS, FSL‐FIRST thalamic segmentation miscalculates thalamic volumetry in a predictable fashion, and may be biased to overestimate highly atrophic thalami. As such, it is recommended that segmentations be reviewed and corrected manually when appropriate for specific studies.</description><identifier>ISSN: 1051-2284</identifier><identifier>EISSN: 1552-6569</identifier><identifier>DOI: 10.1111/jon.12947</identifier><identifier>PMID: 34767684</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Abnormalities ; Algorithms ; Atrophy ; Chi-square test ; Error correction ; errors ; Image classification ; Image processing ; Image segmentation ; Magnetic resonance imaging ; Masks ; Multiple sclerosis ; Neuroimaging ; Reviews ; segmentation ; Thalamus ; volumetry</subject><ispartof>Journal of neuroimaging, 2022-03, Vol.32 (2), p.245-252</ispartof><rights>2021 American Society of Neuroimaging</rights><rights>2021 American Society of Neuroimaging.</rights><rights>2022 American Society of Neuroimaging</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3537-355bf53a88f8f221c18309f914ffed4703f0c403faea9aba5ce1d89f8108fb4c3</citedby><cites>FETCH-LOGICAL-c3537-355bf53a88f8f221c18309f914ffed4703f0c403faea9aba5ce1d89f8108fb4c3</cites><orcidid>0000-0001-7114-4958 ; 0000-0002-7792-0433 ; 0000-0002-7799-1485 ; 0000-0003-4684-4658 ; 0000-0003-0329-3438</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34767684$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lyman, Cassondra</creatorcontrib><creatorcontrib>Lee, Dongchan</creatorcontrib><creatorcontrib>Ferrari, Hannah</creatorcontrib><creatorcontrib>Fuchs, Tom A.</creatorcontrib><creatorcontrib>Bergsland, Niels</creatorcontrib><creatorcontrib>Jakimovski, Dejan</creatorcontrib><creatorcontrib>Weinstock‐Guttmann, Bianca</creatorcontrib><creatorcontrib>Zivadinov, Robert</creatorcontrib><creatorcontrib>Dwyer, Michael G.</creatorcontrib><title>MRI‐based thalamic volumetry in multiple sclerosis using FSL‐FIRST: Systematic assessment of common error modes</title><title>Journal of neuroimaging</title><addtitle>J Neuroimaging</addtitle><description>Background and Purpose
FSL's FMRIB's Integrated Registration and Segmentation Tool (FSL‐FIRST) is a widely used and well‐validated tool. Automated thalamic segmentation is a common application and an important longitudinal measure for multiple sclerosis (MS). However, FSL‐FIRST's algorithm is based on shape models derived from non‐MS groups. As such, the present study sought to systematically assess common thalamic segmentation errors made by FSL‐FIRST on MRIs from people with multiple sclerosis (PwMS).
Methods
FSL‐FIRST was applied to generate thalamic segmentation masks for 890 MR images in PwMS. Images and masks were reviewed systematically to classify and quantify errors, as well as associated anatomical variations and MRI abnormalities. For cases with overt errors (n = 362), thalamic masks were corrected and quantitative volumetric differences were calculated.
Results
In the entire quantitative volumetric group, the mean volumetric error of FSL‐FIRST was 2.74% (0.360 ml): among only corrected cases, the mean volumetric error was 6.79% (0.894 ml). The average percent volumetric error associated with seven error types, two anatomical variants, and motions artifacts are reported. Additional analyses showed that the presence of motion artifacts or anatomical variations significantly increased the probability of error (χ2 = 18.14, p < .01 and χ2 = 64.89, p < .001, respectively). Finally, thalamus volume error was negatively associated with degree of atrophy, such that smaller thalami were systematically overestimated (r = –.28, p < .001).
Conclusions
In PwMS, FSL‐FIRST thalamic segmentation miscalculates thalamic volumetry in a predictable fashion, and may be biased to overestimate highly atrophic thalami. As such, it is recommended that segmentations be reviewed and corrected manually when appropriate for specific studies.</description><subject>Abnormalities</subject><subject>Algorithms</subject><subject>Atrophy</subject><subject>Chi-square test</subject><subject>Error correction</subject><subject>errors</subject><subject>Image classification</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Magnetic resonance imaging</subject><subject>Masks</subject><subject>Multiple sclerosis</subject><subject>Neuroimaging</subject><subject>Reviews</subject><subject>segmentation</subject><subject>Thalamus</subject><subject>volumetry</subject><issn>1051-2284</issn><issn>1552-6569</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kc9u1DAQxi0EoqVw4AWQJS5wSBv_S2xuqGJh0UKlbjlHjjOGrOx48SSgvfEIPCNPgmELByTmMDOH3_dp9A0hj1l9zkpd7NJ0zriR7R1yypTiVaMac7fstWIV51qekAeIu7rmTHJxn5wI2TZto-UpwXfX6x_fvvcWYaDzJxtsHB39ksISYc4HOk40LmEe9wEougA54Yh0wXH6SFfbTZGu1tfbmxd0e8AZop2L2iICYoRppslTl2JME4WcU6YxDYAPyT1vA8Kj23lGPqxe3Vy-qTZXr9eXLzeVE0q0lVCq90pYrb32nDPHtKiNN0x6D4Nsa-FrJ0u3YI3trXLABm28ZrX2vXTijDw7-u5z-rwAzl0c0UEIdoK0YMeVaaXhsjUFffoPuktLnsp1HW9EyzXnRhTq-ZFyJQbM4Lt9HqPNh47V3a9PFNXU_f5EYZ_cOi59hOEv-Sf6Alwcga9jgMP_nbq3V--Plj8BaXiVNg</recordid><startdate>202203</startdate><enddate>202203</enddate><creator>Lyman, Cassondra</creator><creator>Lee, Dongchan</creator><creator>Ferrari, Hannah</creator><creator>Fuchs, Tom A.</creator><creator>Bergsland, Niels</creator><creator>Jakimovski, Dejan</creator><creator>Weinstock‐Guttmann, Bianca</creator><creator>Zivadinov, Robert</creator><creator>Dwyer, Michael G.</creator><general>Wiley Subscription Services, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7114-4958</orcidid><orcidid>https://orcid.org/0000-0002-7792-0433</orcidid><orcidid>https://orcid.org/0000-0002-7799-1485</orcidid><orcidid>https://orcid.org/0000-0003-4684-4658</orcidid><orcidid>https://orcid.org/0000-0003-0329-3438</orcidid></search><sort><creationdate>202203</creationdate><title>MRI‐based thalamic volumetry in multiple sclerosis using FSL‐FIRST: Systematic assessment of common error modes</title><author>Lyman, Cassondra ; Lee, Dongchan ; Ferrari, Hannah ; Fuchs, Tom A. ; Bergsland, Niels ; Jakimovski, Dejan ; Weinstock‐Guttmann, Bianca ; Zivadinov, Robert ; Dwyer, Michael G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3537-355bf53a88f8f221c18309f914ffed4703f0c403faea9aba5ce1d89f8108fb4c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Abnormalities</topic><topic>Algorithms</topic><topic>Atrophy</topic><topic>Chi-square test</topic><topic>Error correction</topic><topic>errors</topic><topic>Image classification</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Magnetic resonance imaging</topic><topic>Masks</topic><topic>Multiple sclerosis</topic><topic>Neuroimaging</topic><topic>Reviews</topic><topic>segmentation</topic><topic>Thalamus</topic><topic>volumetry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lyman, Cassondra</creatorcontrib><creatorcontrib>Lee, Dongchan</creatorcontrib><creatorcontrib>Ferrari, Hannah</creatorcontrib><creatorcontrib>Fuchs, Tom A.</creatorcontrib><creatorcontrib>Bergsland, Niels</creatorcontrib><creatorcontrib>Jakimovski, Dejan</creatorcontrib><creatorcontrib>Weinstock‐Guttmann, Bianca</creatorcontrib><creatorcontrib>Zivadinov, Robert</creatorcontrib><creatorcontrib>Dwyer, Michael G.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of neuroimaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lyman, Cassondra</au><au>Lee, Dongchan</au><au>Ferrari, Hannah</au><au>Fuchs, Tom A.</au><au>Bergsland, Niels</au><au>Jakimovski, Dejan</au><au>Weinstock‐Guttmann, Bianca</au><au>Zivadinov, Robert</au><au>Dwyer, Michael G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MRI‐based thalamic volumetry in multiple sclerosis using FSL‐FIRST: Systematic assessment of common error modes</atitle><jtitle>Journal of neuroimaging</jtitle><addtitle>J Neuroimaging</addtitle><date>2022-03</date><risdate>2022</risdate><volume>32</volume><issue>2</issue><spage>245</spage><epage>252</epage><pages>245-252</pages><issn>1051-2284</issn><eissn>1552-6569</eissn><abstract>Background and Purpose
FSL's FMRIB's Integrated Registration and Segmentation Tool (FSL‐FIRST) is a widely used and well‐validated tool. Automated thalamic segmentation is a common application and an important longitudinal measure for multiple sclerosis (MS). However, FSL‐FIRST's algorithm is based on shape models derived from non‐MS groups. As such, the present study sought to systematically assess common thalamic segmentation errors made by FSL‐FIRST on MRIs from people with multiple sclerosis (PwMS).
Methods
FSL‐FIRST was applied to generate thalamic segmentation masks for 890 MR images in PwMS. Images and masks were reviewed systematically to classify and quantify errors, as well as associated anatomical variations and MRI abnormalities. For cases with overt errors (n = 362), thalamic masks were corrected and quantitative volumetric differences were calculated.
Results
In the entire quantitative volumetric group, the mean volumetric error of FSL‐FIRST was 2.74% (0.360 ml): among only corrected cases, the mean volumetric error was 6.79% (0.894 ml). The average percent volumetric error associated with seven error types, two anatomical variants, and motions artifacts are reported. Additional analyses showed that the presence of motion artifacts or anatomical variations significantly increased the probability of error (χ2 = 18.14, p < .01 and χ2 = 64.89, p < .001, respectively). Finally, thalamus volume error was negatively associated with degree of atrophy, such that smaller thalami were systematically overestimated (r = –.28, p < .001).
Conclusions
In PwMS, FSL‐FIRST thalamic segmentation miscalculates thalamic volumetry in a predictable fashion, and may be biased to overestimate highly atrophic thalami. As such, it is recommended that segmentations be reviewed and corrected manually when appropriate for specific studies.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>34767684</pmid><doi>10.1111/jon.12947</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-7114-4958</orcidid><orcidid>https://orcid.org/0000-0002-7792-0433</orcidid><orcidid>https://orcid.org/0000-0002-7799-1485</orcidid><orcidid>https://orcid.org/0000-0003-4684-4658</orcidid><orcidid>https://orcid.org/0000-0003-0329-3438</orcidid></addata></record> |
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| subjects | Abnormalities Algorithms Atrophy Chi-square test Error correction errors Image classification Image processing Image segmentation Magnetic resonance imaging Masks Multiple sclerosis Neuroimaging Reviews segmentation Thalamus volumetry |
| title | MRI‐based thalamic volumetry in multiple sclerosis using FSL‐FIRST: Systematic assessment of common error modes |
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