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However, methods for routinely quantifying choroid plexus volume are suboptimal and require technical improvements and validation. Here, we propose three deep learning models that can segment the choroid plexus from commonly-acquired anatomical MRI data and report performance metrics and changes across the adult lifespan.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Fully convolutional neural networks were trained from 3D T\n                      <jats:sub>1<\/jats:sub>\n                      -weighted, 3D T\n                      <jats:sub>2<\/jats:sub>\n                      -weighted, and 2D T\n                      <jats:sub>2<\/jats:sub>\n                      -weighted FLAIR MRI using gold-standard manual segmentations in control and neurodegenerative participants across the lifespan (n\u2009=\u200950; age\u2009=\u200921\u201385\u00a0years). Dice coefficients, 95% Hausdorff distances, and area-under-curve (AUCs) were calculated for each model and compared to segmentations from FreeSurfer using two-tailed Wilcoxon tests (significance criteria: p\u2009&lt;\u20090.05 after false discovery rate multiple comparisons correction). Metrics were regressed against lateral ventricular volume using generalized linear models to assess model performance for varying levels of atrophy. Finally, models were applied to an expanded cohort of adult controls (n\u2009=\u200998; age\u2009=\u200921\u201389\u00a0years) to provide an exemplar of choroid plexus volumetry values across the lifespan.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Deep learning results yielded Dice coefficient\u2009=\u20090.72, Hausdorff distance\u2009=\u20091.97\u00a0mm, AUC\u2009=\u20090.87 for T\n                      <jats:sub>1<\/jats:sub>\n                      -weighted MRI, Dice coefficient\u2009=\u20090.72, Hausdorff distance\u2009=\u20092.22\u00a0mm, AUC\u2009=\u20090.87 for T\n                      <jats:sub>2<\/jats:sub>\n                      -weighted MRI, and Dice coefficient\u2009=\u20090.74, Hausdorff distance\u2009=\u20091.69\u00a0mm, AUC\u2009=\u20090.87 for T\n                      <jats:sub>2<\/jats:sub>\n                      -weighted FLAIR MRI; values did not differ significantly between MRI sequences and were statistically improved compared to current commercially-available algorithms (p\u2009&lt;\u20090.001). The intraclass coefficients were 0.95, 0.95, and 0.96 between T\n                      <jats:sub>1<\/jats:sub>\n                      -weighted and T\n                      <jats:sub>2<\/jats:sub>\n                      -weighted FLAIR, T\n                      <jats:sub>1<\/jats:sub>\n                      -weighted and T\n                      <jats:sub>2<\/jats:sub>\n                      -weighted, and T\n                      <jats:sub>2<\/jats:sub>\n                      -weighted and T\n                      <jats:sub>2<\/jats:sub>\n                      -weighted FLAIR models, respectively. Mean lateral ventricle choroid plexus volume across all participants was 3.20\u2009\u00b1\u20091.4 cm\n                      <jats:sup>3<\/jats:sup>\n                      ; a significant, positive relationship (R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.54-0.60) was observed between participant age and choroid plexus volume for all MRI sequences (p\u2009&lt;\u20090.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Findings support comparable performance in choroid plexus delineation between standard, clinically available, non-contrasted anatomical MRI sequences. The software embedding the evaluated models is freely available online and should provide a useful tool for the growing number of studies that desire to quantitatively evaluate choroid plexus structure and function (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/hettk\/chp_seg\">https:\/\/github.com\/hettk\/chp_seg<\/jats:ext-link>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12987-024-00525-9","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T05:03:05Z","timestamp":1709182985000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan"],"prefix":"10.1186","volume":"21","author":[{"given":"Jarrod J.","family":"Eisma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin D.","family":"McKnight","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kilian","family":"Hett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Elenberger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caleb J.","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander K.","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ciaran","family":"Considine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel O.","family":"Claassen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manus J.","family":"Donahue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"key":"525_CR1","doi-asserted-by":"publisher","first-page":"14","DOI":"10.4103\/bc.BC_3_18","volume":"4","author":"AH Khasawneh","year":"2018","unstructured":"Khasawneh AH, Garling RJ, Harris CA. 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