{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T12:08:34Z","timestamp":1773490114808,"version":"3.50.1"},"publisher-location":"Wiesbaden","reference-count":5,"publisher":"Springer Fachmedien Wiesbaden","isbn-type":[{"value":"9783658510992","type":"print"},{"value":"9783658511005","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-658-51100-5_12","type":"book-chapter","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T14:06:01Z","timestamp":1773237961000},"page":"70-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantifying Anatomical Bias in Coronary Segmentation"],"prefix":"10.1007","author":[{"given":"Selina","family":"Baumgart","sequence":"first","affiliation":[]},{"given":"Nikolas","family":"Deubner","sequence":"additional","affiliation":[]},{"given":"Andreas M.","family":"Kist","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Yang S, Kweon J, Roh JH, Lee JH, Kang H, Park LJ et al. Deep learning segmentation of major vessels in X-ray coronary angiography. Sci Rep. 2019;9(1):16897.","DOI":"10.1038\/s41598-019-53254-7"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Meng Y, Du Z, Zhao C, Dong M, Pienta D, Tang J et al. Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms. Catheter Cardiovasc Interv. 2023;31(6):2303\u201317.","DOI":"10.3233\/THC-230278"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Tu S, Holm NR, Koning G, Maeng M, Reiber JHC. The impact of acquisition angle differences on three-dimensional quantitative coronary angiography. JSCI. 2011;78(2):214\u2013 22.","DOI":"10.1002\/ccd.23047"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Kumari V, Kumar N, Kumar KS, Kumar A, Skandha SS, Saxena S et al. Deep learning paradigm and its bias for coronary artery wall segmentation in intravascular ultrasound scans: a closer look. J Cardiovasc Dev Dis. 2023;10(12).","DOI":"10.3390\/jcdd10120485"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Xia S, Zhu H, Liu X, Gong M, Huang X, Xu L et al. Vessel segmentation of X-ray coronary angiographic image sequence. IEEE Trans Biomed Eng. 2020;67(5):1338\u201348.","DOI":"10.1109\/TBME.2019.2936460"}],"container-title":["Informatik aktuell","Bildverarbeitung f\u00fcr die Medizin 2026"],"original-title":[],"language":"de","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-658-51100-5_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:03:57Z","timestamp":1773486237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-658-51100-5_12"}},"subtitle":["Why Your Model Prefers the LCA More Than the RCA"],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783658510992","9783658511005"],"references-count":5,"URL":"https:\/\/doi.org\/10.1007\/978-3-658-51100-5_12","relation":{},"ISSN":["1431-472X","2628-8958"],"issn-type":[{"value":"1431-472X","type":"print"},{"value":"2628-8958","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 March 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"BVM Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"L\u00fcbeck","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Deutschland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 March 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 March 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bvm2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bvm-conf.org\/de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}