{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T20:47:14Z","timestamp":1774385234916,"version":"3.50.1"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T00:00:00Z","timestamp":1642896000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T00:00:00Z","timestamp":1642896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007601","name":"Horizon 2020","doi-asserted-by":"publisher","award":["101017578"],"award-info":[{"award-number":["101017578"]}],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["13GW0372"],"award-info":[{"award-number":["13GW0372"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Careful assessment of the aortic root is paramount to select an appropriate prosthesis for transcatheter aortic valve implantation (TAVI). Relevant information about the aortic root anatomy, such as the aortic annulus diameter, can be extracted from pre-interventional CT. In this work, we investigate a neural network-based approach for segmenting the aortic root as a basis for obtaining these parameters.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>To support valve prosthesis selection, geometric measures of the aortic root are extracted from the patient\u2019s CT scan using a cascade of convolutional neural networks (CNNs). First, the image is reduced to the aortic root, valve, and left ventricular outflow tract (LVOT); within that subimage, the aortic valve and ascending aorta are segmented; and finally, the region around the aortic annulus. From the segmented annulus region, we infer the annulus orientation using principal component analysis (PCA). The area-derived diameter of the annulus is approximated based on the segmentation of the aortic root and LVOT and the plane orientation resulting from the PCA.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The cascade of CNNs was trained using 90 expert-annotated contrast-enhanced CT scans routinely acquired for TAVI planning. Segmentation of the aorta and valve within the region of interest achieved an F1 score of 0.94 on the test set of 36 patients. The area-derived diameter within the annulus region was determined with a mean error below 2\u00a0mm between the automatic measurement and the diameter derived from annotations. The calculated diameters and resulting errors are comparable to published results of alternative approaches.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The cascaded neural network approach enabled the assessment of the aortic root with a relatively small training set. The processing time amounts to 30 s per patient, facilitating time-efficient, reproducible measurements. An extended training data set, including different levels of calcification or special cases (e.g., pre-implanted valves), could further improve this method\u2019s applicability and robustness.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-021-02554-3","type":"journal-article","created":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T11:02:35Z","timestamp":1642935755000},"page":"507-519","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Cascaded neural network-based CT image processing for aortic root analysis"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2688-9480","authenticated-orcid":false,"given":"Nina","family":"Kr\u00fcger","sequence":"first","affiliation":[]},{"given":"Alexander","family":"Meyer","sequence":"additional","affiliation":[]},{"given":"Lennart","family":"Tautz","sequence":"additional","affiliation":[]},{"given":"Markus","family":"H\u00fcllebrand","sequence":"additional","affiliation":[]},{"given":"Isaac","family":"Wamala","sequence":"additional","affiliation":[]},{"given":"Marius","family":"Pullig","sequence":"additional","affiliation":[]},{"given":"Markus","family":"Kofler","sequence":"additional","affiliation":[]},{"given":"J\u00f6rg","family":"Kempfert","sequence":"additional","affiliation":[]},{"given":"Simon","family":"S\u00fcndermann","sequence":"additional","affiliation":[]},{"given":"Volkmar","family":"Falk","sequence":"additional","affiliation":[]},{"given":"Anja","family":"Hennemuth","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,23]]},"reference":[{"key":"2554_CR1","doi-asserted-by":"publisher","unstructured":"Abraham N, Khan NM (2019) A novel focal Tversky loss function with improved attention u-net for lesion segmentation. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pp 683\u2013687. https:\/\/doi.org\/10.1109\/ISBI.2019.8759329","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"2554_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/3591314","volume":"2019","author":"P Astudillo","year":"2019","unstructured":"Astudillo P, Mortier P, Bosmans J, De Backer O, de Jaegere P, De Beule M, Dambre J (2019) Enabling automated device size selection for transcatheter aortic valve implantation. J Interv Cardiol 2019:1\u20137. https:\/\/doi.org\/10.1155\/2019\/3591314","journal-title":"J Interv Cardiol"},{"key":"2554_CR3","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/s10554-015-0793-9","volume":"32","author":"M Elattar","year":"2016","unstructured":"Elattar M, Wiegerinck EM, van Kesteren F, Dubois L, Planken N, Vanhavel E, Baan J, Marquering H (2016) Automatic aortic root landmark detection in CTA images for preprocedural planning of transcatheter aortic valve implantation. Int J Cardiovasc Imaging 32:501\u2013511","journal-title":"Int J Cardiovasc Imaging"},{"key":"2554_CR4","doi-asserted-by":"crossref","unstructured":"Gao X, Kitslaar PH, Reiber JHC (2016) Automatic aortic root segmentation in CTA whole-body dataset. In: Proceedings of SPIE 9785, medical imaging 2016: computer-aided diagnosis, p\u00a08","DOI":"10.1117\/12.2216734"},{"issue":"4","key":"2554_CR5","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.icte.2020.04.010","volume":"6","author":"I Kandel","year":"2020","unstructured":"Kandel I, Castelli M (2020) The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express 6(4):312\u2013315. https:\/\/doi.org\/10.1016\/j.icte.2020.04.010","journal-title":"ICT Express"},{"key":"2554_CR6","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. CoRR arXiv:1412.6980"},{"key":"2554_CR7","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s13239-013-0154-6","volume":"4","author":"M K\u00fctting","year":"2013","unstructured":"K\u00fctting M, Sedaghat A, Tapia AW, Roggenkamp J, Werner N, Schmitz-Rode T, Steinseifer U (2013) Influence of the measurement plane on aortic annulus indices: structural and clinical implications. Cardiovasc Eng Technol 4:513\u2013519. https:\/\/doi.org\/10.1007\/s13239-013-0154-6","journal-title":"Cardiovasc Eng Technol"},{"issue":"3","key":"2554_CR8","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1080\/13645706.2018.1488734","volume":"28","author":"F Lalys","year":"2019","unstructured":"Lalys F, Esneault S, Castro M, Royer L, Haigron P, Auffret V, Tomasi J (2019) Automatic aortic root segmentation and anatomical landmarks detection for TAVI procedure planning. Minim Invasive Ther Allied Technol 28(3):157\u2013164. https:\/\/doi.org\/10.1080\/13645706.2018.1488734","journal-title":"Minim Invasive Ther Allied Technol"},{"issue":"2","key":"2554_CR9","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2020","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318\u2013327. https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2554_CR10","doi-asserted-by":"publisher","first-page":"10746","DOI":"10.1038\/s41598-020-67111-5","volume":"10","author":"A Meyer","year":"2020","unstructured":"Meyer A, Kofler M, Montagner M, Unbehaun A, S\u00fcndermann S, Buz S, Klein C, Stamm C, Solowjowa N, Emmert MY, Falk V, Kempfert J (2020) Reliability and influence on decision making of fully-automated vs. semi-automated software packages for procedural planning in tavi. Sci Rep 10:10746. https:\/\/doi.org\/10.1038\/s41598-020-67111-5","journal-title":"Sci Rep"},{"key":"2554_CR11","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical image computing and computer-assisted intervention\u2014MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical image computing and computer-assisted intervention\u2014MICCAI 2015. Springer International Publishing, Cham, pp 234\u2013241"},{"key":"2554_CR12","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.compmedimag.2018.03.001","volume":"66","author":"HR Roth","year":"2018","unstructured":"Roth HR, Oda H, Zhou X, Shimizu N, Yang Y, Hayashi Y, Oda M, Fujiwara M, Misawa K, Mori K (2018) An application of cascaded 3d fully convolutional networks for medical image segmentation. Comput Med Imaging Graph 66:90\u201399. https:\/\/doi.org\/10.1016\/j.compmedimag.2018.03.001","journal-title":"Comput Med Imaging Graph"},{"key":"2554_CR13","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-319-67389-9_44","volume-title":"Machine learning in medical imaging","author":"SSM Salehi","year":"2017","unstructured":"Salehi SSM, Erdogmus D, Gholipour A (2017) Tversky loss function for image segmentation using 3d fully convolutional deep networks. In: Wang Q, Shi Y, Suk HI, Suzuki K (eds) Machine learning in medical imaging. Springer International Publishing, Cham, pp 379-387"},{"key":"2554_CR14","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1007\/s00330-014-3199-5","volume":"24","author":"A Schuhbaeck","year":"2014","unstructured":"Schuhbaeck A, Achenbach S, Pflederer T, Marwan M, Schmid J, Nef H, Rixe J, Hecker F, Schneider C, Lell M, Uder M, Arnold M (2014) Reproducibility of aortic annulus measurements by computed tomography. Eur Radiol 24:1887\u20131888. https:\/\/doi.org\/10.1007\/s00330-014-3199-5","journal-title":"Eur Radiol"},{"key":"2554_CR15","doi-asserted-by":"publisher","unstructured":"Waechter I, Kneser R, Korosoglou G, Peters J, Bakker NH, Boomen Rvd, Weese J (2010). Patient specific models for planning and guidance of minimally invasive aortic valve implantation, pp 526\u2013533. https:\/\/doi.org\/10.1007\/978-3-642-15705-9_64","DOI":"10.1007\/978-3-642-15705-9_64"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02554-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-021-02554-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02554-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T12:20:20Z","timestamp":1645705220000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-021-02554-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,23]]},"references-count":15,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["2554"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02554-3","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,23]]},"assertion":[{"value":"25 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Need for informed consent was waived due to the retrospective nature of the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Not publicly available.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}