{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T01:41:37Z","timestamp":1790127697350,"version":"4.0.1"},"posted":{"date-parts":[[2024,8,31]]},"group-title":"Surgery","reference-count":33,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T00:00:00Z","timestamp":1725062400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,1,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  The scale and resolution of anatomical features extracted from medical CT images are crucial for advancing clinical decision-making tools. While traditional metrics, such as maximum aortic diameter, have long been the standard for classifying aortic diseases, these one-dimensional measures often fall short in capturing the rich geometrical nuances available in progressively advancing imaging modalities. Recent advancements in computational methods and imaging have introduced more sophisticated geometric signatures, in particular scale-invariant measures of aortic shape. Among these, the normalized fluctuation in total integrated Gaussian curvature\n                  <jats:inline-formula>\n                    <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"24312310v2_inline1.gif\"\/>\n                  <\/jats:inline-formula>\n                  over a surface mesh model of the aorta has emerged as a particularly promising metric. However, there exists a critical tradeoff between noise reduction and shape signal preservation within the scale space parameters \u2013 namely, smoothing intensity, meshing density, and partitioning size. Through a comprehensive analysis of over 1200 unique scale space constructions derived from a cohort of 185 aortic dissection patients, this work pinpoints optimal resolution scales at which shape variations are most strongly correlated with surgical outcomes. Importantly, these findings emphasize the pivotal role of a secondary discretization step, which consistently yield the most robust signal when scaled to approximately 1 cm. This approach enables the development of models that are not only clinically effective but also inherently resilient to biases introduced by patient population heterogeneity. By focusing on the appropriate intermediate scales for analysis, this study paves the way for more precise and reliable tools in medical imaging, ultimately contributing to improved patient outcomes in cardiovascular surgery.\n                <\/jats:p>","DOI":"10.1101\/2024.08.30.24312310","type":"posted-content","created":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T14:15:16Z","timestamp":1725113716000},"source":"Crossref","is-referenced-by-count":0,"title":["Thoracic Aortic Shape: A Data-Driven Scale Space Approach"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3295-5664","authenticated-orcid":false,"given":"Joseph A.","family":"Pugar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9117-4628","authenticated-orcid":false,"given":"Junsung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3164-6133","authenticated-orcid":false,"given":"Kameel","family":"Khabaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2652-9714","authenticated-orcid":false,"given":"Luka","family":"Pocivavsek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2025013102400966000_2024.08.30.24312310v2.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2013.10.015"},{"issue":"1","key":"2025013102400966000_2024.08.30.24312310v2.2","first-page":"1","article-title":"Aortic dissection","volume":"2","year":"2016","journal-title":"Nat Rev Dis Primers"},{"key":"2025013102400966000_2024.08.30.24312310v2.3","doi-asserted-by":"publisher","DOI":"10.1136\/heartjnl-2019-316251"},{"key":"2025013102400966000_2024.08.30.24312310v2.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2018.11.017"},{"issue":"7","key":"2025013102400966000_2024.08.30.24312310v2.5","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1109\/JBHI.2020.2991043","article-title":"AI in Medical Imaging Informatics: Current Challenges and Future Directions","volume":"24","year":"2020","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"2025013102400966000_2024.08.30.24312310v2.6","doi-asserted-by":"crossref","unstructured":"A. Senapati , S. Khalaf , and M. H. Al-Mallah , \u201cContemporary advances in medical imaging,\u201d in Machine Learning in Cardiovascular Medicine, S. J. Al\u2019Aref , G. Singh , L. Baskaran , and D. Metaxas , Eds., Academic Press, 2021, pp. 149\u2013176.","DOI":"10.1016\/B978-0-12-820273-9.00007-5"},{"key":"2025013102400966000_2024.08.30.24312310v2.7","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5931-z"},{"key":"2025013102400966000_2024.08.30.24312310v2.8","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28257"},{"key":"2025013102400966000_2024.08.30.24312310v2.9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-53459-5"},{"key":"2025013102400966000_2024.08.30.24312310v2.10","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2024.3355307"},{"issue":"2","key":"2025013102400966000_2024.08.30.24312310v2.11","doi-asserted-by":"crossref","first-page":"e1011815","DOI":"10.1371\/journal.pcbi.1011815","article-title":"The geometric evolution of aortic dissections: Predicting surgical success using fluctuations in integrated Gaussian curvature","volume":"20","year":"2024","journal-title":"PLOS Computational Biology"},{"key":"2025013102400966000_2024.08.30.24312310v2.12","doi-asserted-by":"crossref","unstructured":"S. Rusinkiewicz , \u201cEstimating curvatures and their derivatives on triangle meshes,\u201d in Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004., Sep. 2004, pp. 486\u2013493.","DOI":"10.1109\/TDPVT.2004.1335277"},{"key":"2025013102400966000_2024.08.30.24312310v2.13","doi-asserted-by":"publisher","DOI":"10.1007\/BF00336961"},{"key":"2025013102400966000_2024.08.30.24312310v2.14","doi-asserted-by":"crossref","unstructured":"A. P. Witkin , \u201cSCALE-SPACE FILTERING,\u201d in Readings in Computer Vision, M. A. Fischler and O. Firschein , Eds., San Francisco (CA): Morgan Kaufmann, 1987, pp. 329\u2013332.","DOI":"10.1016\/B978-0-08-051581-6.50036-2"},{"key":"2025013102400966000_2024.08.30.24312310v2.15","unstructured":"J. J. Koenderink , Solid Shape. 1990."},{"issue":"2","key":"2025013102400966000_2024.08.30.24312310v2.16","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","year":"2004","journal-title":"International Journal of Computer Vision"},{"key":"2025013102400966000_2024.08.30.24312310v2.17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1986.4767749"},{"key":"2025013102400966000_2024.08.30.24312310v2.18","doi-asserted-by":"publisher","DOI":"10.1006\/jvci.1993.1001"},{"key":"2025013102400966000_2024.08.30.24312310v2.19","doi-asserted-by":"crossref","unstructured":"T. Hou and H. Qin , \u201cEfficient Computation of Scale-Space Features for Deformable Shape Correspondences,\u201d in Computer Vision \u2013 ECCV 2010, K. Daniilidis, P. Maragos, and N. Paragios, Eds., Berlin, Heidelberg: Springer, 2010, pp. 384\u2013397.","DOI":"10.1007\/978-3-642-15558-1_28"},{"key":"2025013102400966000_2024.08.30.24312310v2.20","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-012-0528-5"},{"key":"2025013102400966000_2024.08.30.24312310v2.21","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12429"},{"key":"2025013102400966000_2024.08.30.24312310v2.22","doi-asserted-by":"publisher","DOI":"10.1109\/2945.817348"},{"key":"2025013102400966000_2024.08.30.24312310v2.23","unstructured":"Q.-Y. Zhou , J. Park , and V. Koltun , Open3D: A Modern Library for 3D Data Processing. (2018)."},{"key":"2025013102400966000_2024.08.30.24312310v2.24","unstructured":"D. Haggerty , trimesh. (Dec. 08, 2019). [Online]. Available: https:\/\/trimesh.org\/"},{"key":"2025013102400966000_2024.08.30.24312310v2.25","doi-asserted-by":"crossref","unstructured":"M. Garland and P. S. Heckbert , \u201cSurface simplification using quadric error metrics,\u201d in Proceedings of the 24th annual conference on Computer graphics and interactive techniques, in SIGGRAPH \u201897. USA: ACM Press\/Addison-Wesley Publishing Co., Aug. 1997, pp. 209\u2013216.","DOI":"10.1145\/258734.258849"},{"key":"2025013102400966000_2024.08.30.24312310v2.26","doi-asserted-by":"publisher","DOI":"10.1007\/BF01908075"},{"key":"2025013102400966000_2024.08.30.24312310v2.27","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0224365"},{"key":"2025013102400966000_2024.08.30.24312310v2.28","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.182.3.1820593"},{"key":"2025013102400966000_2024.08.30.24312310v2.29","doi-asserted-by":"publisher","DOI":"10.1136\/heartjnl-2021-320574"},{"key":"2025013102400966000_2024.08.30.24312310v2.30","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.56.889"},{"key":"2025013102400966000_2024.08.30.24312310v2.31","doi-asserted-by":"publisher","DOI":"10.1051\/jphys:01986004703050700"},{"key":"2025013102400966000_2024.08.30.24312310v2.32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2013.05.031"},{"key":"2025013102400966000_2024.08.30.24312310v2.33","doi-asserted-by":"publisher","DOI":"10.21037\/jtd-20-1643"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2024.08.30.24312310","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T20:10:40Z","timestamp":1768507840000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.1101\/2024.08.30.24312310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,31]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1101\/2024.08.30.24312310","relation":{},"subject":[],"published":{"date-parts":[[2024,8,31]]},"subtype":"preprint"}}