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First, a segmentation network delineates renal structures in high\u2010contrast images. Next, a deep learning\u2010based pairwise image registration algorithm maps the multiparametric image series to a common target and transfers the predicted annotations between the multiparametric images. Finally, clinically relevant quantitative parameters are extracted through region\u2010specific assessment of renal structure and function based on the aligned and segmented multiparametric data. We used five\u2010fold cross\u2010validation to compare the segmentation outcomes and extracted features with manual analyses in 24 patients with prostate cancer or neuroendocrine tumors and 10 healthy subjects, each undergoing repeated scans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Our automated pipeline achieved high agreement with expert kidney segmentation while delivering significant alignment improvements through registration. Volumetric analysis showed a strong correlation (\n                      <jats:italic>r<\/jats:italic>\n                      0.9) with manual results, and feature extraction demonstrated high intraclass correlation coefficients with minimal bias. The complete processing pipeline, encompassing coregistration, segmentation, and feature extraction, required approximately 15\u2009s per scan from raw input to final quantitative output.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The study establishes a reliable automated pipeline for renal multiparametric MRI postprocessing. The achieved accuracy and efficiency can support improved diagnosis and treatment planning for patients with kidney disease.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70288","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T04:50:58Z","timestamp":1770267058000},"page":"3519-3535","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated Coregistered Segmentation for Volumetric Analysis of Multiparametric Renal\n                    <scp>MRI<\/scp>"],"prefix":"10.1002","volume":"95","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1221-1756","authenticated-orcid":false,"given":"Aya","family":"Ghoul","sequence":"first","affiliation":[{"name":"Medical Image and Data Analysis (MIDAS.Lab), Department of Interventional and Diagnostic Radiology University Hospital of Tuebingen  Tuebingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6937-1681","authenticated-orcid":false,"given":"Cecilia","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Interventional and Diagnostic Radiology University Hospital of Tuebingen  Tuebingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabelle","family":"Loster","sequence":"additional","affiliation":[{"name":"Department of Interventional and Diagnostic Radiology University Hospital of Tuebingen  Tuebingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7224-0930","authenticated-orcid":false,"given":"Lavanya","family":"Umapathy","sequence":"additional","affiliation":[{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernd","family":"K\u00fchn","sequence":"additional","affiliation":[{"name":"Siemens Healthineers AG  Erlangen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petros","family":"Martirosian","sequence":"additional","affiliation":[{"name":"Department of Interventional and Diagnostic Radiology University Hospital of Tuebingen  Tuebingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9696-2954","authenticated-orcid":false,"given":"Ferdinand","family":"Seith","sequence":"additional","affiliation":[{"name":"Department of Interventional and Diagnostic Radiology University Hospital of Tuebingen  Tuebingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6928-4967","authenticated-orcid":false,"given":"Sergios","family":"Gatidis","sequence":"additional","affiliation":[{"name":"Medical Image and Data Analysis (MIDAS.Lab), Department of Interventional and Diagnostic Radiology University Hospital of Tuebingen  Tuebingen Germany"},{"name":"Department of Radiology Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0353-4898","authenticated-orcid":false,"given":"Thomas","family":"K\u00fcstner","sequence":"additional","affiliation":[{"name":"Medical Image and Data Analysis (MIDAS.Lab), Department of Interventional and Diagnostic Radiology University Hospital of Tuebingen  Tuebingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2017.00696"},{"key":"e_1_2_10_3_1","unstructured":"C.Anna P.Menno B.Michel andM.Selby Nicholas \u201cFunctional Magnetic Resonance Imaging of the Kidneys: Where Do We Stand? 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