{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T07:57:46Z","timestamp":1773302266384,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013923","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000}}],"reference-count":70,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014989","name":"Chan Zuckerberg Initiative","doi-asserted-by":"publisher","award":["DAF2022-316777 and CZIF2024-009938"],"award-info":[{"award-number":["DAF2022-316777 and CZIF2024-009938"]}],"id":[{"id":"10.13039\/100014989","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["UM1-NS132358"],"award-info":[{"award-number":["UM1-NS132358"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["310796\/Z\/24\/Z"],"award-info":[{"award-number":["310796\/Z\/24\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"name":"International Alliance for Cancer Early Detection,","award":["EDDAPA-2023\/100002"],"award-info":[{"award-number":["EDDAPA-2023\/100002"]}]},{"DOI":"10.13039\/501100000287","name":"Royal Academy of Engineering","doi-asserted-by":"crossref","award":["CiET1819\/10"],"award-info":[{"award-number":["CiET1819\/10"]}],"id":[{"id":"10.13039\/501100000287","id-type":"DOI","asserted-by":"crossref"}]},{"name":"CIFAR MacMillan Multiscale Human Fellowship"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"\n Biomedical systems span multiple spatial scales, encompassing tiny functional units to entire organs. Interpreting these systems through image segmentation requires the effective propagation and integration of information across different scales. However, most existing segmentation methods are optimised for single-scale imaging modalities, limiting their ability to capture and analyse small functional units throughout complete human organs. To facilitate multiscale biomedical image segmentation, we utilised Hierarchical Phase-Contrast Tomography (HiP-CT), an advanced imaging modality that can generate 3D multiscale datasets from high-resolution volumes of interest (VOIs) at ca. 1\n \n \n \n \n \n \u03bc<\/mml:mi>\n m<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n \/voxel to whole-organ scans at ca. 20\n \n \n \n \n \n \u03bc<\/mml:mi>\n m<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n \/voxel. Building on these hierarchical multiscale datasets, we developed a deep learning-based segmentation pipeline that is initially trained on manually annotated high-resolution HiP-CT data and then extended to lower-resolution whole-organ scans using pseudo-labels generated from high-resolution predictions and multiscale image registration. As a case study, we focused on glomeruli in human kidneys, benchmarking four 3D deep learning models for biomedical image segmentation on a manually annotated high-resolution dataset extracted from VOIs, at 2.58 to ca. 5\n \n \n \n \n \n \u03bc<\/mml:mi>\n m<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n \/voxel, of four human kidneys. Among them, nnUNet demonstrated the best performance, achieving an average test Dice score of 0.906, and was subsequently used as the baseline model for multiscale segmentation in the pipeline. Applying this pipeline to two low-resolution full-organ data at ca. 25\n \n \n \n \n \n \u03bc<\/mml:mi>\n m<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n \/voxel, the model identified 1,019,890 and 231,179 glomeruli in a 62-year-old donor without kidney diseases and a 94-year-old hypertensive donor, enabling comprehensive morphological analyses, including cortical spatial statistics and glomerular distributions, which aligned well with previous anatomical studies. Our results highlight the effectiveness of the proposed pipeline for segmenting small functional units in multiscale bioimaging datasets and suggest its broader applicability to other organ systems.\n <\/jats:p>","DOI":"10.1371\/journal.pcbi.1013923","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T19:01:22Z","timestamp":1770058882000},"page":"e1013923","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multiscale segmentation using hierarchical phase-contrast tomography and deep learning"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5474-9605","authenticated-orcid":true,"given":"Yang","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Shahab","family":"Aslani","sequence":"additional","affiliation":[]},{"given":"Yousef","family":"Javanmardi","sequence":"additional","affiliation":[]},{"given":"Joseph","family":"Brunet","sequence":"additional","affiliation":[]},{"given":"David","family":"Stansby","sequence":"additional","affiliation":[]},{"given":"Saskia","family":"Carroll","sequence":"additional","affiliation":[]},{"given":"Alexandre","family":"Bellier","sequence":"additional","affiliation":[]},{"given":"Maximilian","family":"Ackermann","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5962-1683","authenticated-orcid":true,"given":"Paul","family":"Tafforeau","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3898-8881","authenticated-orcid":true,"given":"Peter D.","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3769-3392","authenticated-orcid":true,"given":"Claire L.","family":"Walsh","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"issue":"1","key":"pcbi.1013923.ref001","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1038\/s41467-024-54591-6","article-title":"Functional tissue units in the Human Reference Atlas","volume":"16","author":"S Bidanta","year":"2025","journal-title":"Nat Commun."},{"issue":"11","key":"pcbi.1013923.ref002","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1005217","article-title":"Creating high-resolution multiscale maps of human tissue using multi-beam SEM","volume":"12","author":"AF Pereira","year":"2016","journal-title":"PLoS Comput Biol."},{"issue":"5","key":"pcbi.1013923.ref003","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1009074","article-title":"A deep learning algorithm for 3D cell detection in whole mouse brain image datasets","volume":"17","author":"AL Tyson","year":"2021","journal-title":"PLoS Comput Biol."},{"issue":"1","key":"pcbi.1013923.ref004","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/jmri.28067","article-title":"Magnetic resonance imaging versus computed tomography for three-dimensional bone imaging of musculoskeletal pathologies: a review","volume":"56","author":"MC Florkow","year":"2022","journal-title":"J Magn Reson Imaging."},{"issue":"12","key":"pcbi.1013923.ref005","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1038\/s41592-021-01317-x","article-title":"Imaging intact human organs with local resolution of cellular structures using hierarchical phase-contrast tomography","volume":"18","author":"CL Walsh","year":"2021","journal-title":"Nat Methods."},{"key":"pcbi.1013923.ref006","doi-asserted-by":"crossref","unstructured":"Jain Y, Walsh CL, Yagis E, Aslani S, Nandanwar S, Zhou Y. 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