{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:08:01Z","timestamp":1787789281410,"version":"build-2784847793"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:00:00Z","timestamp":1772841600000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Collaboration for New Treatments of Acute Stroke"},{"DOI":"10.13039\/501100002996","name":"Dutch Heart Foundation","doi-asserted-by":"crossref","award":["2012\u00a0T077"],"award-info":[{"award-number":["2012\u00a0T077"]}],"id":[{"id":"10.13039\/501100002996","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002996","name":"Dutch Heart Foundation","doi-asserted-by":"crossref","award":["2012\u00a0T077"],"award-info":[{"award-number":["2012\u00a0T077"]}],"id":[{"id":"10.13039\/501100002996","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002996","name":"Dutch Heart Foundation","doi-asserted-by":"crossref","award":["2016\u00a0T086"],"award-info":[{"award-number":["2016\u00a0T086"]}],"id":[{"id":"10.13039\/501100002996","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001826","name":"Netherlands Organization for Health Research and Development","doi-asserted-by":"crossref","award":["015.008.048"],"award-info":[{"award-number":["015.008.048"]}],"id":[{"id":"10.13039\/501100001826","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Netherlands Cardiovascular Research Initiative"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Precise volumetric evaluation of intracerebral haemorrhage (ICH), intraventricular haemorrhage (IVH) and perihaematomal oedema (PHO) is essential but manual segmentation is time-consuming and susceptible to variability. We aimed to develop and externally validate a deep learning model for simultaneous segmentation of ICH, IVH and PHO on non-contrast CT (NCCT) in patients with spontaneous ICH.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients and methods<\/jats:title>\n                    <jats:p>A 3D U-net model was trained with 5-fold cross-validation on baseline NCCTs from 301 patients included in 2 prospective multicentre studies. External validation was performed on 141 baseline NCCTs from another multicentre study. Model performance was evaluated against manual ground truth segmentations using the Dice similarity coefficient (DSC), intraclass correlation coefficients (ICC) and Bland\u2013Altman analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved a median DSC of 0.93 (IQR 0.91\u20130.94) for ICH, 0.75 (IQR 0.57\u20130.82) for IVH and 0.53 (IQR 0.34\u20130.65) for PHO. Volume correlations were excellent for ICH (mean absolute and consistency ICC both 0.98 [95% CI 0.98\u20130.99]) and IVH (absolute ICC 0.97 [95% CI 0.92\u20130.98]; consistency ICC 0.98 [95% CI 0.96\u20130.99]), and moderate for PHO (absolute ICC 0.60 [95% CI -0.08\u20130.85]; consistency ICC 0.82 [95% CI 0.76\u20130.87]). Bland\u2013Altman analyses demonstrated a bias for ICH of \u22120.48\u00a0mL (LoA \u22128.21 to 7.26), for IVH of \u22121.68\u00a0mL (LoA \u22127.35 to 3.99) and for PHO of 13.91\u00a0mL (LoA \u22124.85 to 32.68).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and conclusion<\/jats:title>\n                    <jats:p>The model enables accurate automated segmentation of ICH, while IVH and PHO segmentation remain more challenging. Automated segmentations may already serve as reliable pre-segmentations in research, but require visual assessment and correction, in particular for IVH and PHO.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/esj\/aakag007","type":"journal-article","created":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T02:31:09Z","timestamp":1772850669000},"source":"Crossref","is-referenced-by-count":2,"title":["Deep learning-based automated segmentation of intracerebral haemorrhage, intraventricular haemorrhage and perihaematomal oedema on non-contrast CT"],"prefix":"10.1093","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1964-1962","authenticated-orcid":false,"given":"Floor N H","family":"Wilting","sequence":"first","affiliation":[{"name":"Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center , Nijmegen ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8232-576X","authenticated-orcid":false,"given":"Jules P J","family":"Douwes","sequence":"additional","affiliation":[{"name":"Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht , Utrecht ,","place":["The Netherlands"]},{"name":"Department of Otorhinolaryngology-Head and Neck Surgery, Leiden University Medical Center , Leiden ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajay","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Radboud University Medical Center , Nijmegen ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7815-0207","authenticated-orcid":false,"given":"Floris H B M","family":"Schreuder","sequence":"additional","affiliation":[{"name":"Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center , Nijmegen ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7033-0644","authenticated-orcid":false,"given":"Ruben","family":"Dammers","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Erasmus University Medical Center, Erasmus MC Stroke Center, Center for Complex Microvascular Surgery , Rotterdam ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9526-3775","authenticated-orcid":false,"given":"Gerjon","family":"Hannink","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Radboud University Medical Center , Nijmegen ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wilmar M T","family":"Jolink","sequence":"additional","affiliation":[{"name":"Department of Neurology, Isala Hospital , Zwolle ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9314-6137","authenticated-orcid":false,"given":"Sjoert A H","family":"Pegge","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Radboud University Medical Center , Nijmegen ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2037-7484","authenticated-orcid":false,"given":"Lotte","family":"Sondag","sequence":"additional","affiliation":[{"name":"Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center , Nijmegen ,","place":["The Netherlands"]},{"name":"Department of Neurology, Jeroen Bosch Hospital , \u2018s-Hertogenbosch ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6838-9521","authenticated-orcid":false,"given":"Marieke J H","family":"Wermer","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Medical Center Groningen , Groningen ,","place":["The Netherlands"]},{"name":"Department of Neurology, Leiden University Medical Center , Leiden ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9891-2136","authenticated-orcid":false,"given":"H Bart","family":"van der Worp","sequence":"additional","affiliation":[{"name":"Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht , Utrecht ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5921-639X","authenticated-orcid":false,"given":"Frederick J A","family":"Meijer","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Radboud University Medical Center , Nijmegen ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8495-4578","authenticated-orcid":false,"given":"Catharina J M","family":"Klijn","sequence":"additional","affiliation":[{"name":"Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center , Nijmegen ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,3,7]]},"reference":[{"key":"2026032317433216900_ref1","doi-asserted-by":"publisher","first-page":"101180","DOI":"10.1016\/j.lanepe.2024.101180","article-title":"Incidence, case fatality, and functional outcome of intracerebral haemorrhage, according to age, sex, and country income level: a systematic review and meta-analysis","volume":"49","author":"Wolsink","year":"2025","journal-title":"Lancet Reg Health Eur"},{"key":"2026032317433216900_ref2","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1016\/S1474-4422(12)70104-7","article-title":"Intracerebral haemorrhage: mechanisms of injury and therapeutic targets","volume":"11","author":"Keep","year":"2012","journal-title":"Lancet Neurol"},{"key":"2026032317433216900_ref3","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1161\/01.STR.29.7.1352","article-title":"Hydrocephalus: a previously unrecognized predictor of poor outcome from supratentorial intracerebral hemorrhage","volume":"29","author":"Diringer","year":"1998","journal-title":"Stroke."},{"key":"2026032317433216900_ref4","doi-asserted-by":"publisher","first-page":"e000003","DOI":"10.1136\/svn-2015-000003","article-title":"Mechanisms of hydrocephalus after intraventricular haemorrhage in adults","volume":"1","author":"Bu","year":"2016","journal-title":"Stroke Vasc Neurol"},{"key":"2026032317433216900_ref5","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1055\/s-0036-1582132","article-title":"Stages of the inflammatory response in pathology and tissue repair after intracerebral hemorrhage","volume":"36","author":"Askenase","year":"2016","journal-title":"Semin Neurol"},{"key":"2026032317433216900_ref6","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1161\/01.str.24.7.987","article-title":"Volume of intracerebral hemorrhage: a powerful and easy-to-use predictor of 30-day mortality","volume":"24","author":"Broderick","year":"1993","journal-title":"Stroke."},{"key":"2026032317433216900_ref7","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1212\/wnl.40.4.616","article-title":"Prognostic significance of ventricular blood in supratentorial hemorrhage: a volumetric study","volume":"40","author":"Young","year":"1990","journal-title":"Neurology."},{"key":"2026032317433216900_ref8","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1177\/23969873231157884","article-title":"The association between perihaematomal oedema and functional outcome after spontaneous intracerebral haemorrhage: a systematic review and meta-analysis","volume":"8","author":"Cliteur","year":"2023","journal-title":"Eur Stroke J"},{"key":"2026032317433216900_ref9","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1002\/jnr.24372","article-title":"Perihematomal edema: implications for intracerebral hemorrhage research and therapeutic advances","volume":"98","author":"Selim","year":"2020","journal-title":"J Neurosci Res"},{"key":"2026032317433216900_ref10","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.1161\/01.STR.27.8.1304","article-title":"The ABCs of measuring intracerebral hemorrhage volumes","volume":"27","author":"Kothari","year":"1996","journal-title":"Stroke."},{"key":"2026032317433216900_ref11","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1161\/STROKEAHA.112.670653","article-title":"The modified Graeb score: an enhanced tool for intraventricular hemorrhage measurement and prediction of functional outcome","volume":"44","author":"Morgan","year":"2013","journal-title":"Stroke."},{"key":"2026032317433216900_ref12","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1161\/01.STR.0000198806.67472.5c","article-title":"Comparison of ABC\/2 estimation technique to computer-assisted planimetric analysis in warfarin-related intracerebral parenchymal hemorrhage","volume":"37","author":"Huttner","year":"2006","journal-title":"Stroke."},{"key":"2026032317433216900_ref13","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1080\/02841850802647039","article-title":"Volume-dependent overestimation of spontaneous intracerebral hematoma volume by the ABC\/2 formula","volume":"50","author":"Wang","year":"2009","journal-title":"Acta Radiol"},{"key":"2026032317433216900_ref14","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","article-title":"Deep learning techniques for medical image segmentation: achievements and challenges","volume":"32","author":"Hesamian","year":"2019","journal-title":"J Digit Imaging"},{"key":"2026032317433216900_ref15","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s41747-018-0061-6","article-title":"Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine","volume":"2","author":"Pesapane","year":"2018","journal-title":"Eur Radiol Exp"},{"key":"2026032317433216900_ref16","doi-asserted-by":"publisher","DOI":"10.1161\/svin.123.001314","article-title":"Do deep learning algorithms accurately segment intracerebral hemorrhages on noncontrast computed tomography? A systematic review and meta-analysis","volume":"4","author":"Zarei","year":"2024","journal-title":"Stroke Vasc Interv Neurol"},{"key":"2026032317433216900_ref17","doi-asserted-by":"publisher","first-page":"911207","DOI":"10.3389\/fimmu.2022.911207","article-title":"Defining delayed perihematomal Edema expansion in intracerebral Hemorrhage: segmentation, time course, risk factors and clinical outcome","volume":"13","author":"Chen","year":"2022","journal-title":"Front Immunol"},{"key":"2026032317433216900_ref18","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.3390\/bioengineering11121274","article-title":"A hybrid transformer-convolutional neural network for segmentation of intracerebral Hemorrhage and perihematomal Edema on non-contrast head computed tomography (CT) with uncertainty quantification to improve confidence","volume":"11","author":"Tran","year":"2024","journal-title":"Bioengineering (Basel)"},{"key":"2026032317433216900_ref19","doi-asserted-by":"publisher","first-page":"19389","DOI":"10.1038\/s41598-020-76459-7","article-title":"A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT","volume":"10","author":"Arab","year":"2020","journal-title":"Sci Rep"},{"key":"2026032317433216900_ref20","doi-asserted-by":"publisher","first-page":"105546","DOI":"10.1016\/j.cmpb.2020.105546","article-title":"Psi-net: focusing on the border areas of intracerebral hemorrhage on CT images","volume":"194","author":"Kuang","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"2026032317433216900_ref21","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/s12021-020-09493-5","article-title":"3D deep neural network segmentation of intracerebral Hemorrhage: development and validation for clinical trials","volume":"19","author":"Sharrock","year":"2021","journal-title":"Neuroinformatics."},{"key":"2026032317433216900_ref22","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1186\/s12880-021-00657-6","article-title":"Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement","volume":"21","author":"Wang","year":"2021","journal-title":"BMC Med Imaging"},{"key":"2026032317433216900_ref23","doi-asserted-by":"publisher","first-page":"965680","DOI":"10.3389\/fnins.2022.965680","article-title":"Deep learning-based computed tomography image segmentation and volume measurement of intracerebral hemorrhage","volume":"16","author":"Peng","year":"2022","journal-title":"Front Neurosci"},{"key":"2026032317433216900_ref24","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1111\/jon.12997","article-title":"Bayesian deep learning outperforms clinical trial estimators of intracerebral and intraventricular hemorrhage volume","volume":"32","author":"Sharrock","year":"2022","journal-title":"J Neuroimaging"},{"key":"2026032317433216900_ref25","doi-asserted-by":"publisher","first-page":"4005","DOI":"10.3390\/jcm12124005","article-title":"External validation and retraining of DeepBleed: the first open-source 3D deep learning network for the segmentation of spontaneous intracerebral and intraventricular Hemorrhage","volume":"12","author":"Cao","year":"2023","journal-title":"J Clin Med"},{"key":"2026032317433216900_ref26","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1097\/RCT.0000000000001380","article-title":"Intracerebral Hemorrhage segmentation on noncontrast computed tomography using a masked loss function U-net approach","volume":"47","author":"Coorens","year":"2023","journal-title":"J Comput Assist Tomogr"},{"key":"2026032317433216900_ref27","doi-asserted-by":"publisher","first-page":"107334","DOI":"10.1016\/j.compbiomed.2023.107334","article-title":"CHSNet: automatic lesion segmentation network guided by CT image features for acute cerebral hemorrhage","volume":"164","author":"Xu","year":"2023","journal-title":"Comput Biol Med"},{"key":"2026032317433216900_ref28","doi-asserted-by":"publisher","first-page":"8317","DOI":"10.1002\/mp.17343","article-title":"Deep learning models for separate segmentations of intracerebral and intraventricular hemorrhage on head CT and segmentation quality assessment","volume":"51","author":"Li","year":"2024","journal-title":"Med Phys"},{"key":"2026032317433216900_ref29","doi-asserted-by":"publisher","first-page":"106507","DOI":"10.1016\/j.neunet.2024.106507","article-title":"Multi-scale object equalization learning network for intracerebral hemorrhage region segmentation","volume":"179","author":"Zhang","year":"2024","journal-title":"Neural Netw"},{"key":"2026032317433216900_ref30","doi-asserted-by":"publisher","first-page":"2776","DOI":"10.1161\/STROKEAHA.116.013779","article-title":"Development and validation of an automatic segmentation algorithm for quantification of intracerebral Hemorrhage","volume":"47","author":"Scherer","year":"2016","journal-title":"Stroke."},{"key":"2026032317433216900_ref31","doi-asserted-by":"publisher","first-page":"3416","DOI":"10.1161\/STROKEAHA.119.026561","article-title":"Fully automated segmentation algorithm for hematoma volumetric analysis in spontaneous intracerebral Hemorrhage","volume":"50","author":"Ironside","year":"2019","journal-title":"Stroke."},{"key":"2026032317433216900_ref32","doi-asserted-by":"publisher","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries.","author":"Islam","DOI":"10.1007\/978-3-030-11723-8_46"},{"key":"2026032317433216900_ref33","doi-asserted-by":"publisher","first-page":"17858","DOI":"10.1038\/s41598-019-54491-6","article-title":"Intracerebral haemorrhage segmentation in non-contrast CT","volume":"9","author":"Patel","year":"2019","journal-title":"Sci Rep"},{"key":"2026032317433216900_ref34","doi-asserted-by":"publisher","first-page":"102352","DOI":"10.1016\/j.ipm.2020.102352","article-title":"Automatic segmentation of intracerebral hemorrhage in CT images using encoder\u2013decoder convolutional neural network","volume":"57","author":"Hu","year":"2020","journal-title":"Inf Process Manag"},{"key":"2026032317433216900_ref35","doi-asserted-by":"publisher","first-page":"101908","DOI":"10.1016\/j.compmedimag.2021.101908","article-title":"Hemorrhagic stroke lesion segmentation using a 3D U-net with squeeze-and-excitation blocks","volume":"90","author":"Abramova","year":"2021","journal-title":"Comput Med Imaging Graph"},{"key":"2026032317433216900_ref36","doi-asserted-by":"publisher","first-page":"e328","DOI":"10.1016\/j.crad.2022.12.019","article-title":"Deep-learning measurement of intracerebral haemorrhage with mixed precision training: a coarse-to-fine study","volume":"78","author":"Jiang","year":"2023","journal-title":"Clin Radiol"},{"key":"2026032317433216900_ref37","doi-asserted-by":"publisher","first-page":"3440","DOI":"10.1038\/s41598-023-30581-4","article-title":"Evaluation of grouped capsule network for intracranial hemorrhage segmentation in CT scans","volume":"13","author":"Wang","year":"2023","journal-title":"Sci Rep"},{"key":"2026032317433216900_ref38","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1161\/STROKEAHA.119.027657","article-title":"Deep learning for automated measurement of Hemorrhage and perihematomal Edema in supratentorial intracerebral Hemorrhage","volume":"51","author":"Dhar","year":"2020","journal-title":"Stroke."},{"key":"2026032317433216900_ref39","doi-asserted-by":"publisher","first-page":"e220096","DOI":"10.1148\/ryai.220096","article-title":"Semantic segmentation of spontaneous intracerebral Hemorrhage, intraventricular Hemorrhage, and associated Edema on CT images using deep learning","volume":"4","author":"Kok","year":"2022","journal-title":"Radiol Artif Intell"},{"key":"2026032317433216900_ref40","doi-asserted-by":"publisher","first-page":"5012","DOI":"10.1007\/s00330-020-07558-2","article-title":"Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema","volume":"31","author":"Zhao","year":"2021","journal-title":"Eur Radiol"},{"key":"2026032317433216900_ref41","doi-asserted-by":"publisher","first-page":"e213642","DOI":"10.1212\/WNL.0000000000213642","article-title":"Cortical cerebral microinfarcts in spontaneous intracerebral Hemorrhage on 3T and 7T MRI","volume":"105","author":"Jolink","year":"2025","journal-title":"Neurology."},{"key":"2026032317433216900_ref42","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1016\/S1474-4422(21)00298-2","article-title":"Apixaban versus no anticoagulation after anticoagulation-associated intracerebral haemorrhage in patients with atrial fibrillation in the Netherlands (APACHE-AF): a randomised, open-label, phase 2 trial","volume":"20","author":"Schreuder","year":"2021","journal-title":"Lancet Neurol"},{"key":"2026032317433216900_ref43","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1007\/s00701-023-05599-2","article-title":"Safety and technical efficacy of early minimally invasive endoscopy-guided surgery for intracerebral haemorrhage: the Dutch intracerebral haemorrhage surgery trial pilot study","volume":"165","author":"Sondag","year":"2023","journal-title":"Acta Neurochir"},{"key":"2026032317433216900_ref44","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat Methods"},{"key":"2026032317433216900_ref45","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/S1076-6332(03)00671-8","article-title":"Statistical validation of image segmentation quality based on a spatial overlap index","volume":"11","author":"Zou","year":"2004","journal-title":"Acad Radiol"},{"key":"2026032317433216900_ref46","doi-asserted-by":"publisher","first-page":"055012","DOI":"10.1088\/1361-6579\/ab86d6","article-title":"A comprehensive guideline for bland-Altman and intra class correlation calculations to properly compare two methods of measurement and interpret findings","volume":"41","author":"Haghayegh","year":"2020","journal-title":"Physiol Meas"},{"key":"2026032317433216900_ref47","doi-asserted-by":"publisher","DOI":"10.4324\/9780203774441","volume-title":"Applied multiple regression\/correlation analysis for the behavioral sciences","author":"Cohen","year":"2003","edition":"3rd"},{"key":"2026032317433216900_ref48","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.1161\/STROKEAHA.116.015062","article-title":"Noncontrast computed tomography markers of intracerebral Hemorrhage expansion","volume":"48","author":"Boulouis","year":"2017","journal-title":"Stroke."},{"key":"2026032317433216900_ref49","doi-asserted-by":"publisher","DOI":"10.1093\/radadv\/umaf012","article-title":"Cross-institutional automated multilabel segmentation for acute intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT","volume":"2","author":"Nawabi","year":"2025","journal-title":"Radiol Adv"},{"key":"2026032317433216900_ref50","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1111\/j.1468-1331.2011.03395.x","article-title":"Semi-automatic volumetric assessment of perihemorrhagic edema with computed tomography","volume":"18","author":"Volbers","year":"2011","journal-title":"Eur J Neurol"},{"key":"2026032317433216900_ref51","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1148\/radiol.2421052066","article-title":"Multi-detector row CT attenuation measurements: assessment of intra- and interscanner variability with an anthropomorphic body CT phantom","volume":"242","author":"Birnbaum","year":"2007","journal-title":"Radiology."},{"key":"2026032317433216900_ref52","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1016\/j.ajem.2018.07.016","article-title":"Inter-scanner variability in Hounsfield unit measured by CT of the brain and effect on gray-to-white matter ratio","volume":"37","author":"Oh","year":"2019","journal-title":"Am J Emerg Med"},{"key":"2026032317433216900_ref53","doi-asserted-by":"publisher","first-page":"e240597","DOI":"10.1148\/radiol.240597","article-title":"Foundation models in radiology: what, how, why, and why not","volume":"314","author":"Paschali","year":"2025","journal-title":"Radiology."}],"container-title":["European Stroke Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/esj\/article-pdf\/11\/3\/aakag007\/67263051\/aakag007.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/esj\/article-pdf\/11\/3\/aakag007\/67263051\/aakag007.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T21:43:38Z","timestamp":1774302218000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/esj\/article\/doi\/10.1093\/esj\/aakag007\/8509791"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,1]]},"references-count":53,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3,1]]}},"URL":"https:\/\/doi.org\/10.1093\/esj\/aakag007","relation":{},"ISSN":["2396-9873","2396-9881"],"issn-type":[{"value":"2396-9873","type":"print"},{"value":"2396-9881","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,3]]},"published":{"date-parts":[[2026,3,1]]},"article-number":"aakag007"}}