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The alternations of CSF production and the ChP can be associated with the Alzheimer\u2019s disease (AD). The present work investigated the roles of the ChP in the AD based on a proposed ChP image segmentation pipeline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A human-in-the-loop ChP image segmentation pipeline was implemented with intermediate and active learning datasets. The performance of the proposed pipeline was evaluated on manual contours by five radiologists, compared to the FreeSurfer and FastSurfer toolboxes. The ChP volume and blood flow were investigated among AD groups. The correlations between the ChP volume and AD CSF biomarkers including phosphorylated tau (p-tau), total tau (t-tau), amyloid-\u03b242 (A\u03b242), and amyloid-\u03b240 (A\u03b240) was investigated using three models (univariate, multiple variables, and stepwise regression) on two datasets with 806 and 320 subjects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The proposed ChP segmentation pipeline achieved superior performance with a Dice coefficient of 0.620 on the test dataset, compared to the FreeSurfer (0.342) and FastSurfer (0.371). Significantly larger volumes (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) and higher perfusion (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.032) at the ChP were found in AD compared to CN groups. Significant correlations were found between the tau and the relative ChP volume (the ChP volume and ChP\/parenchyma ratio) in each patient groups and in the univariate regression analysis (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), the multiple regression model (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05 except for the t-tau in the LMCI), and in the step-wise regression model (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.021). In addition, the correlation coefficients changed from \u2212\u00a00.32 to \u2212\u00a00.21 along with the AD progression in the multiple regression model. In contrast, the A\u03b242 and A\u03b240 shows consistent and significant associations with the lateral ventricle related measures in the step-wise regression model (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.027).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      The proposed pipeline provided accurate ChP segmentation which revealed the associations between the ChP and tau level in the AD. The proposed pipeline is available on GitHub (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/princeleeee\/ChP-Seg\">https:\/\/github.com\/princeleeee\/ChP-Seg<\/jats:ext-link>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12987-024-00554-4","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T08:02:35Z","timestamp":1720771355000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Associations between the choroid plexus and tau in Alzheimer\u2019s disease using an active learning segmentation pipeline"],"prefix":"10.1186","volume":"21","author":[{"given":"Jiaxin","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueqin","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunzhi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Craig H.","family":"Meyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiying","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"issue":"147","key":"554_CR1","first-page":"147ra111","volume":"4","author":"JJ Iliff","year":"2012","unstructured":"Iliff JJ, Wang M, Liao Y, Plogg BA, Peng W, Gundersen GA, et al. 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