04339nam a22006375i 4500 978-3-031-53241-2 DE-He213 20251030111421.0 cr nn 008mamaa 240209s2024 sz | s |||| 0|eng d 9783031532412 978-3-031-53241-2 10.1007/978-3-031-53241-2 doi TA1501-1820 TA1634 UYT bicssc COM016000 bisacsh UYT thema Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition [electronic resource] : First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / edited by Antonio Pepe, Gian Marco Melito, Jan Egger. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XII, 142 p. 74 illus., 67 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Lecture Notes in Computer Science, 1611-3349 ; 14539 M3F: 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. This 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. Image processing Digital techniques. 64228 Computer vision. Image processing. Social sciences Data processing. 65741 Computers. 65793 Education Data processing. Computer Imaging, Vision, Pattern Recognition and Graphics. 65550 Image Processing. Computer Application in Social and Behavioral Sciences. 65743 Computing Milieux. 66530 Computers and Education. Pepe, Antonio. editor. edt http://id.loc.gov/vocabulary/relators/edt 70827 Melito, Gian Marco. editor. edt http://id.loc.gov/vocabulary/relators/edt 70828 Egger, Jan. editor. edt http://id.loc.gov/vocabulary/relators/edt 70829 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031532405 Printed edition: 9783031532429 Lecture Notes in Computer Science, 1611-3349 ; 14539 https://doi.org/10.1007/978-3-031-53241-2 ZDB-2-SCS ZDB-2-SXCS ZDB-2-LNC 57559 57559