Automatic quantification of normal cortical folding patterns from fetal brain MRI
We automatically quantify patterns of normal cortical folding in the developing fetus from in utero MR images (N=80) over a wide gestational age (GA) range (21.7 to 38.9weeks). This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related...
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description | We automatically quantify patterns of normal cortical folding in the developing fetus from in utero MR images (N=80) over a wide gestational age (GA) range (21.7 to 38.9weeks). This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related to pathology, facilitating earlier diagnosis and intervention. The cortical boundary was delineated by automatically segmenting the brain MR image into a number of key structures. This utilised a spatio-temporal atlas as tissue priors in an expectation–maximization approach with second order Markov random field (MRF) regularization to improve the accuracy of the cortical boundary estimate. An implicit high resolution surface was then used to compute cortical folding measures. We validated the automated segmentations with manual delineations and the average surface discrepancy was of the order of 1mm. Eight curvature-based folding measures were computed for each fetal cortex and used to give summary shape descriptors. These were strongly correlated with GA (R2=0.99) confirming the close link between neurological development and cortical convolution. This allowed an age-dependent non-linear model to be accurately fitted to the folding measures. The model supports visual observations that, after a slow initial start, cortical folding increases rapidly between 25 and 30weeks and subsequently slows near birth. The model allows the accurate prediction of fetal age from an observed folding measure with a smaller error where growth is fastest. We also analysed regional patterns in folding by parcellating each fetal cortex using a nine-region anatomical atlas and found that Gompertz models fitted the change in lobar regions. Regional differences in growth rate were detected, with the parietal and posterior temporal lobes exhibiting the fastest growth, while the cingulate, frontal and medial temporal lobes developed more slowly.
•We automatically quantify cortical folding in 80 normal foetuses (22–38weeks GA).•Eight curvature-based cortical folding measures were evaluated for each foetus.•Folding measures were better correlated with GA than volume and GA.•A Gompertz function accurately models the increase in folding measures with GA.•This model allows an accurate prediction of GA from folding measures. |
doi_str_mv | 10.1016/j.neuroimage.2014.01.034 |
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•We automatically quantify cortical folding in 80 normal foetuses (22–38weeks GA).•Eight curvature-based cortical folding measures were evaluated for each foetus.•Folding measures were better correlated with GA than volume and GA.•A Gompertz function accurately models the increase in folding measures with GA.•This model allows an accurate prediction of GA from folding measures.</description><identifier>ISSN: 1053-8119</identifier><identifier>EISSN: 1095-9572</identifier><identifier>DOI: 10.1016/j.neuroimage.2014.01.034</identifier><identifier>PMID: 24473102</identifier><language>eng</language><publisher>Amsterdam: Elsevier Inc</publisher><subject>Adult ; Algorithms ; Atlases as Topic ; Automation ; Biological and medical sciences ; Brain development ; Cerebral Cortex - anatomy & histology ; Cerebral Cortex - embryology ; Cerebral Cortex - growth & development ; Cortical folding ; Data Interpretation, Statistical ; Female ; Fetal MRI ; Fetus - anatomy & histology ; Fetuses ; Fundamental and applied biological sciences. Psychology ; Gestational Age ; Gompertz function ; Humans ; Image Processing, Computer-Assisted ; Linear Models ; Magnetic Resonance Imaging - methods ; NMR ; Nuclear magnetic resonance ; Pregnancy ; Reproducibility of Results ; Studies ; Vertebrates: nervous system and sense organs</subject><ispartof>NeuroImage (Orlando, Fla.), 2014-05, Vol.91, p.21-32</ispartof><rights>2014 Elsevier Inc.</rights><rights>2015 INIST-CNRS</rights><rights>Copyright © 2014 Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited May 1, 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c515t-57e222d29cfa9669f741d605864d04638d617aeeadd6eb08d736136604ac90c83</citedby><cites>FETCH-LOGICAL-c515t-57e222d29cfa9669f741d605864d04638d617aeeadd6eb08d736136604ac90c83</cites><orcidid>0000-0001-7731-1227</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S105381191400055X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28456158$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24473102$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wright, R.</creatorcontrib><creatorcontrib>Kyriakopoulou, V.</creatorcontrib><creatorcontrib>Ledig, C.</creatorcontrib><creatorcontrib>Rutherford, M.A.</creatorcontrib><creatorcontrib>Hajnal, J.V.</creatorcontrib><creatorcontrib>Rueckert, D.</creatorcontrib><creatorcontrib>Aljabar, P.</creatorcontrib><title>Automatic quantification of normal cortical folding patterns from fetal brain MRI</title><title>NeuroImage (Orlando, Fla.)</title><addtitle>Neuroimage</addtitle><description>We automatically quantify patterns of normal cortical folding in the developing fetus from in utero MR images (N=80) over a wide gestational age (GA) range (21.7 to 38.9weeks). This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related to pathology, facilitating earlier diagnosis and intervention. The cortical boundary was delineated by automatically segmenting the brain MR image into a number of key structures. This utilised a spatio-temporal atlas as tissue priors in an expectation–maximization approach with second order Markov random field (MRF) regularization to improve the accuracy of the cortical boundary estimate. An implicit high resolution surface was then used to compute cortical folding measures. We validated the automated segmentations with manual delineations and the average surface discrepancy was of the order of 1mm. Eight curvature-based folding measures were computed for each fetal cortex and used to give summary shape descriptors. These were strongly correlated with GA (R2=0.99) confirming the close link between neurological development and cortical convolution. This allowed an age-dependent non-linear model to be accurately fitted to the folding measures. The model supports visual observations that, after a slow initial start, cortical folding increases rapidly between 25 and 30weeks and subsequently slows near birth. The model allows the accurate prediction of fetal age from an observed folding measure with a smaller error where growth is fastest. We also analysed regional patterns in folding by parcellating each fetal cortex using a nine-region anatomical atlas and found that Gompertz models fitted the change in lobar regions. Regional differences in growth rate were detected, with the parietal and posterior temporal lobes exhibiting the fastest growth, while the cingulate, frontal and medial temporal lobes developed more slowly.
•We automatically quantify cortical folding in 80 normal foetuses (22–38weeks GA).•Eight curvature-based cortical folding measures were evaluated for each foetus.•Folding measures were better correlated with GA than volume and GA.•A Gompertz function accurately models the increase in folding measures with GA.•This model allows an accurate prediction of GA from folding measures.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Atlases as Topic</subject><subject>Automation</subject><subject>Biological and medical sciences</subject><subject>Brain development</subject><subject>Cerebral Cortex - anatomy & histology</subject><subject>Cerebral Cortex - embryology</subject><subject>Cerebral Cortex - growth & development</subject><subject>Cortical folding</subject><subject>Data Interpretation, Statistical</subject><subject>Female</subject><subject>Fetal MRI</subject><subject>Fetus - anatomy & histology</subject><subject>Fetuses</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Gestational Age</subject><subject>Gompertz function</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Linear Models</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Pregnancy</subject><subject>Reproducibility of Results</subject><subject>Studies</subject><subject>Vertebrates: nervous system and sense organs</subject><issn>1053-8119</issn><issn>1095-9572</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqNkd9rFDEQgBex2Fr9F2RBBF92zewm2eSxFn8UKqVFn0MumZQcu8k1yRb8781xpwVf9CkT5ptMZr6maYH0QIB_2PYB1xT9ou-xHwjQnkBPRvqsOQMiWSfZNDzfx2zsBIA8bV7mvCWESKDiRXM6UDqNQIaz5vZiLXHRxZv2YdWheOdNvcXQRteGmBY9tyammq-Bi7P14b7d6VIwhdy6FJfWYam5TdI-tN_url41J07PGV8fz_Pmx-dP3y-_dtc3X64uL647w4CVjk04DIMdpHFaci7dRMFywgSnllA-Csth0ojaWo4bIuw0chg5J1QbSYwYz5v3h3d3KT6smItafDY4zzpgXLMCxrmgcpim_0ChrmYEKSv69i90G9cU6iAKOBVkopzTSokDZVLMOaFTu1RlpJ8KiNobUlv1ZEjtDSkCqhqqpW-ODdbNgvZP4W8lFXh3BHSuS3dJB-PzEyco48D28388cFiX_OgxqWw8BoPWJzRF2ej__ZtfMCqyYg</recordid><startdate>20140501</startdate><enddate>20140501</enddate><creator>Wright, R.</creator><creator>Kyriakopoulou, V.</creator><creator>Ledig, C.</creator><creator>Rutherford, M.A.</creator><creator>Hajnal, J.V.</creator><creator>Rueckert, D.</creator><creator>Aljabar, P.</creator><general>Elsevier Inc</general><general>Elsevier</general><general>Elsevier Limited</general><scope>IQODW</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>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</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>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>M7P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>7QO</scope><orcidid>https://orcid.org/0000-0001-7731-1227</orcidid></search><sort><creationdate>20140501</creationdate><title>Automatic quantification of normal cortical folding patterns from fetal brain MRI</title><author>Wright, R. ; Kyriakopoulou, V. ; Ledig, C. ; Rutherford, M.A. ; Hajnal, J.V. ; Rueckert, D. ; Aljabar, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c515t-57e222d29cfa9669f741d605864d04638d617aeeadd6eb08d736136604ac90c83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Atlases as Topic</topic><topic>Automation</topic><topic>Biological and medical sciences</topic><topic>Brain development</topic><topic>Cerebral Cortex - anatomy & histology</topic><topic>Cerebral Cortex - embryology</topic><topic>Cerebral Cortex - growth & development</topic><topic>Cortical folding</topic><topic>Data Interpretation, Statistical</topic><topic>Female</topic><topic>Fetal MRI</topic><topic>Fetus - anatomy & histology</topic><topic>Fetuses</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Gestational Age</topic><topic>Gompertz function</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted</topic><topic>Linear Models</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>NMR</topic><topic>Nuclear magnetic resonance</topic><topic>Pregnancy</topic><topic>Reproducibility of Results</topic><topic>Studies</topic><topic>Vertebrates: nervous system and sense organs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wright, R.</creatorcontrib><creatorcontrib>Kyriakopoulou, V.</creatorcontrib><creatorcontrib>Ledig, C.</creatorcontrib><creatorcontrib>Rutherford, M.A.</creatorcontrib><creatorcontrib>Hajnal, J.V.</creatorcontrib><creatorcontrib>Rueckert, D.</creatorcontrib><creatorcontrib>Aljabar, P.</creatorcontrib><collection>Pascal-Francis</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>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech 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>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</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>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest Psychology</collection><collection>Biological Science 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>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><jtitle>NeuroImage (Orlando, Fla.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wright, R.</au><au>Kyriakopoulou, V.</au><au>Ledig, C.</au><au>Rutherford, M.A.</au><au>Hajnal, J.V.</au><au>Rueckert, D.</au><au>Aljabar, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic quantification of normal cortical folding patterns from fetal brain MRI</atitle><jtitle>NeuroImage (Orlando, Fla.)</jtitle><addtitle>Neuroimage</addtitle><date>2014-05-01</date><risdate>2014</risdate><volume>91</volume><spage>21</spage><epage>32</epage><pages>21-32</pages><issn>1053-8119</issn><eissn>1095-9572</eissn><abstract>We automatically quantify patterns of normal cortical folding in the developing fetus from in utero MR images (N=80) over a wide gestational age (GA) range (21.7 to 38.9weeks). This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related to pathology, facilitating earlier diagnosis and intervention. The cortical boundary was delineated by automatically segmenting the brain MR image into a number of key structures. This utilised a spatio-temporal atlas as tissue priors in an expectation–maximization approach with second order Markov random field (MRF) regularization to improve the accuracy of the cortical boundary estimate. An implicit high resolution surface was then used to compute cortical folding measures. We validated the automated segmentations with manual delineations and the average surface discrepancy was of the order of 1mm. Eight curvature-based folding measures were computed for each fetal cortex and used to give summary shape descriptors. These were strongly correlated with GA (R2=0.99) confirming the close link between neurological development and cortical convolution. This allowed an age-dependent non-linear model to be accurately fitted to the folding measures. The model supports visual observations that, after a slow initial start, cortical folding increases rapidly between 25 and 30weeks and subsequently slows near birth. The model allows the accurate prediction of fetal age from an observed folding measure with a smaller error where growth is fastest. We also analysed regional patterns in folding by parcellating each fetal cortex using a nine-region anatomical atlas and found that Gompertz models fitted the change in lobar regions. Regional differences in growth rate were detected, with the parietal and posterior temporal lobes exhibiting the fastest growth, while the cingulate, frontal and medial temporal lobes developed more slowly.
•We automatically quantify cortical folding in 80 normal foetuses (22–38weeks GA).•Eight curvature-based cortical folding measures were evaluated for each foetus.•Folding measures were better correlated with GA than volume and GA.•A Gompertz function accurately models the increase in folding measures with GA.•This model allows an accurate prediction of GA from folding measures.</abstract><cop>Amsterdam</cop><pub>Elsevier Inc</pub><pmid>24473102</pmid><doi>10.1016/j.neuroimage.2014.01.034</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-7731-1227</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adult Algorithms Atlases as Topic Automation Biological and medical sciences Brain development Cerebral Cortex - anatomy & histology Cerebral Cortex - embryology Cerebral Cortex - growth & development Cortical folding Data Interpretation, Statistical Female Fetal MRI Fetus - anatomy & histology Fetuses Fundamental and applied biological sciences. Psychology Gestational Age Gompertz function Humans Image Processing, Computer-Assisted Linear Models Magnetic Resonance Imaging - methods NMR Nuclear magnetic resonance Pregnancy Reproducibility of Results Studies Vertebrates: nervous system and sense organs |
title | Automatic quantification of normal cortical folding patterns from fetal brain MRI |
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