{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T20:38:32Z","timestamp":1793392712366,"version":"4.5.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:sec>\n                    <jats:title>Abstract<\/jats:title>\n                    <jats:p>High-quality segmentation is important for AI-driven radiological research and clinical practice, with the potential to play an even more prominent role in the future. As medical imaging advances, accurately segmenting anatomical and pathological structures is increasingly used to obtain quantitative data and valuable insights. Segmentation and volumetric analysis could enable more precise diagnosis, treatment planning, and patient monitoring. These guidelines aim to improve segmentation accuracy and consistency, allowing for better decision-making in both research and clinical environments. Practical advice on planning and organization is provided, focusing on quality, precision, and communication among clinical teams. Additionally, tips and strategies for improving segmentation practices in radiology and radiation oncology are discussed, as are potential pitfalls to avoid.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>\n                            <jats:italic>As AI continues to advance, volumetry will become more integrated into clinical practice, making it essential for radiologists to stay informed about its applications in diagnosis and treatment planning<\/jats:italic>\n                            .\n                          <\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\n                            <jats:italic>There is a significant lack of practical guidelines and resources tailored specifically for radiologists on technical topics like segmentation and volumetric analysis<\/jats:italic>\n                            .\n                          <\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\n                            <jats:italic>Establishing clear rules and best practices for segmentation can streamline volumetric assessment in clinical settings, making it easier to manage and leading to more accurate decision-making for patient care<\/jats:italic>\n                            .\n                          <\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-11621-1","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T03:15:53Z","timestamp":1747883753000},"page":"6894-6904","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["ESR Essentials: a step-by-step guide of segmentation for radiologists\u2014practice recommendations by the European Society of Medical Imaging Informatics"],"prefix":"10.1007","volume":"35","author":[{"given":"Kalina","family":"Chupetlovska","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tugba","family":"Akinci D\u2019Antonoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuhir","family":"Bodalal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed A.","family":"Abdelatty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hendrik","family":"Erenstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o","family":"Santinha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Merel","family":"Huisman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob J.","family":"Visser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Trebeschi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3651-2529","authenticated-orcid":false,"given":"Kevin B. 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She has not taken part in the peer review or the selection process of this article. M.H. reports speakers honoraria from industry; paid consulting (Capvision); support for attending meetings and\/or travel from scientific societies; EuSoMII Board member, ESR eHealth & Informatics Subcommittee member, ECR Imaging Informatics\/Artificial Intelligence and Machine Learning Chairperson 2025, committee member with FMS (Dutch), and Radiology: Artificial Intelligence associate editor and trainee editorial board advisory panel (all unpaid). J.J.V. received a grant to institution from Qure.ai\/HealthHolland\/Enlitic; consulting fees from Tegus; payment to institution for lectures from Roche; travel grant from Qure.ai; participation on a data safety monitoring board or advisory board from Contextflow, Noaber Foundation, and NLC ventures; leadership or fiduciary role on the steering committee of the PINPOINT Project (payment to institution from AstraZeneca) and RSNA Common Data Elements Steering Committee (unpaid); phantom shares in Contextflow and Quibim; chair scientific committee EuSoMII (unpaid); chair ESR value-based radiology subcommittee (unpaid); and member editorial board European Journal of Radiology (unpaid). K.B.W.G.L. is chairman of the young club of EuSoMII (unpaid) and associate editor at BJR Open (unpaid). The other authors report no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was not required.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was not required.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}