04146nam a22005895i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001600172050001100188072001600199072002300215072001500238245034700253250001800600264006900618300006500687336002600752337002600778338003600804347002400840490005900864505095200923520072301875650004902598650002102647650002202668650004502690650002202735650003202757650007102789650002202860650006702882650003002949650002902979700008303008700008803091700008003179710003403259773002603293776003603319776003603355830005903391856004603450912001403496912001503510912001403525999001703539978-3-031-53241-2DE-He21320251030111421.0cr nn 008mamaa240209s2024    sz |    s    |||| 0|eng d  a97830315324129978-3-031-53241-27 a10.1007/978-3-031-53241-22doi 4aTA1501-1820 4aTA1634 7aUYT2bicssc 7aCOM0160002bisacsh 7aUYT2thema10aSegmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisitionh[electronic resource] :bFirst Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings /cedited by Antonio Pepe, Gian Marco Melito, Jan Egger.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXII, 142 p. 74 illus., 67 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aLecture Notes in Computer Science,x1611-3349 ;v145390 aM3F: Multi-Field-of-View Feature Fusion Network for Aortic Vessel Tree Segmentation in CT Angiography -- Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge -- A Data-Centric Approach for Segmenting the Aortic Vessel Tree: A Solution to SEG.A. Challenge 2023 Segmentation Task -- Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge -- Position-encoded pixel-to-prototype contrastive learning for aortic vessel tree segmentation -- Misclassification Loss for Segmentation of the Aortic Vessel Tree -- Deep Learning-based segmentation and mesh reconstruction of the Aortic Vessel Tree from CTA images -- RASNet: U-Net-based Robust Aortic Segmentation Network For Multicenter Datasets -- Optimizing Aortic Segmentation with an Innovative Quality Assessment: The Role of Global Sensitivity Analysis -- A mini tutorial on mesh generation of blood vessels for CFD applications.  aThis book constitutes the First Segmentation of the Aorta Challenge, SEG.A. 2023, which was held in conjunction with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, on October 8, 2023. The 8 full and 3 short papers presented have been carefully reviewed and selected for inclusion in the book. They focus specifically on robustness, visual quality and meshing of automatically generated segmentations of aortic vessel trees from CT imaging. The challenge was organized as a ”container submission” challenge, where participants had to upload their algorithms to Grand Challenge in the form of Docker containers. Three tasks were created for SEG.A. 2023. 0aImage processingxDigital techniques.964228 0aComputer vision. 0aImage processing. 0aSocial sciencesxData processing.965741 0aComputers.965793 0aEducationxData processing.14aComputer Imaging, Vision, Pattern Recognition and Graphics.96555024aImage Processing.24aComputer Application in Social and Behavioral Sciences.96574324aComputing Milieux.96653024aComputers and Education.1 aPepe, Antonio.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9708271 aMelito, Gian Marco.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9708281 aEgger, Jan.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9708292 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303153240508iPrinted edition:z9783031532429 0aLecture Notes in Computer Science,x1611-3349 ;v1453940uhttps://doi.org/10.1007/978-3-031-53241-2  aZDB-2-SCS  aZDB-2-SXCS  aZDB-2-LNC  c57559d57559