{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T01:04:57Z","timestamp":1793667897385,"version":"4.5.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"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:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Overscanning is a common issue in CT planning, leading to unnecessary radiation exposure.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a deep learning model to segment anatomical structures in scout views to optimize scan ranges and reduce radiation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>In this single-center retrospective study, 1146 patients undergoing CT between 2022 and 2025 were included. The model was trained on segmentations of 26 target structures in five regions (head, neck, chest, chest-to-pelvis, abdomen-to-pelvis), transferred to scout views and manually corrected. Performance was evaluated using the Dice-S\u00f8rensen coefficient and normalized surface distance on an internal test set of 100 patients and 36 external chest CTs. Automated versus manual scan planning was compared in 61 internal (chest, upper abdomen, head) and 14 external (chest) CTs, with z-axis coverage and dose-length product.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      1146 patients (mean age, 63\u2009\u00b1\u200917 years; 577 men) were included. For target structures in five regions, mean DSC and NSD were 0.93 and 0.88. External mean DSC across chest targets was 0.851\u2009\u00b1\u20090.051. Automated planning captured relevant anatomy in 98% of internal and 92.9% of external CTs. Scan length significantly decreased for automated planning in the internal test cohort (chest 50\u2009mm (15%),\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001; upper abdomen 60\u2009mm (25%),\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001; cranial 19\u2009mm (11%),\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), yielding corresponding DLP reductions of 19%, 25% and 11%, respectively. In the external cohort, scan length decreased by 115\u2009mm (28.7%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001) with a corresponding 28.5% DLP reduction.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed model enables reliable automated CT scan planning and reduces overscanning and radiation exposure without compromising diagnostic quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Can segmentation of anatomical structures on CT scout views enable automated scan planning to reduce overscanning and unnecessary radiation exposure?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>A deep learning model segmented planning-relevant anatomy on CT scout views and reduced scan length and dose while preserving anatomical coverage.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Unnecessary radiation from overscanning is a patient safety concern. Automated CT planning with the proposed deep learning model reduces radiation exposure while ensuring full anatomical coverage for the evaluated scan regions.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12623-3","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T15:36:08Z","timestamp":1778772968000},"page":"7698-7709","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["TOPOS: target organ prediction on scout views for automated CT scan planning"],"prefix":"10.1007","volume":"36","author":[{"given":"Sebastian","family":"Ziegelmayer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4269-142X","authenticated-orcid":false,"given":"Tristan","family":"Lemke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Graf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su Hwan","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas","family":"Lemke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian J.","family":"Mertens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Busch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominik","family":"Weller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus R.","family":"Makowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa C.","family":"Adams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keno K.","family":"Bressem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"12623_CR1","doi-asserted-by":"publisher","first-page":"2078","DOI":"10.1001\/archinternmed.2009.427","volume":"169","author":"R Smith-Bindman","year":"2009","unstructured":"Smith-Bindman R, Lipson J, Marcus R et al (2009) Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. 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The remaining authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"One of the authors (S.Z.) has significant statistical expertise.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Not applicable.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Approval was granted by the Ethics Committee of the Technical University of Munich.","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Not applicable.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}