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Automatic solutions would help enhance their quality and reproducibility.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To design a deep learning (DL)\u2010based automated approach for aortic landmarks and lumen detection derived from three\u2010dimensional (3D) MRI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>Three hundred ninety\u2010one individuals (female: 47%, age\u2009=\u200951.9\u2009\u00b1\u200918.4) from three sites, including healthy subjects and patients (hypertension, aortic dilation, Turner syndrome), randomly divided into training\/validation\/test datasets (N\u2009=\u2009236\/77\/78). Twenty\u2010five subjects were randomly selected and analyzed by three operators with different levels of expertise.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>1.5\u2010T and 3\u2010T, 3D spoiled gradient\u2010recalled or steady\u2010state free precession sequences.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Reinforcement learning and a two\u2010stage network trained using reference landmarks and segmentation from an existing semi\u2010automatic software were used for aortic landmark detection and segmentation from sinotubular junction to coeliac trunk. Aortic segments were defined using the detected landmarks while the aortic centerline was extracted from the segmentation and morphological indices (length, aortic diameter, and volume) were computed for both the reference and the proposed segmentations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Segmentation: Dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetrical surface distance (ASSD); landmark detection: Euclidian distance (ED); model robustness: Spearman correlation, Bland\u2013Altman analysis, Kruskal\u2013Wallis test for comparisons between reference and DL\u2010derived aortic indices; inter\u2010observer study: Williams index (WI). A WI 95% confidence interval (CI) lower bound &gt;1 indicates that the method is within the inter\u2010observer variability. A <jats:italic>P<\/jats:italic>\u2010value &lt;0.05 was considered statistically significant.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>DSC was 0.90\u2009\u00b1\u20090.05, HD was 12.11\u2009\u00b1\u20097.79\u2009mm, and ASSD was 1.07\u2009\u00b1\u20090.63\u2009mm. ED was 5.0\u2009\u00b1\u20096.1\u2009mm. A good agreement was found between all DL\u2010derived and reference aortic indices (<jats:italic>r<\/jats:italic>\u2009&gt;0.95, mean bias &lt;7%). Our segmentation and landmark detection performances were within the inter\u2010observer variability except the sinotubular junction landmark (CI\u2009=\u20090.96;1.04).<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>A DL\u2010based aortic segmentation and anatomical landmark detection approach was developed and applied to 3D MRI data for achieve aortic morphology evaluation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>3<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29236","type":"journal-article","created":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T03:59:46Z","timestamp":1705118386000},"page":"1565-1576","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep Learning\u2010Based Analysis of Aortic Morphology From Three\u2010Dimensional 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