TY - BOOK ID - 143152797 TI - Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition : First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings AU - Pepe, Antonio AU - Melito, Gian Marco AU - Egger, Jan PY - 2024 SN - 3031532414 PB - Cham : Springer Nature Switzerland : Imprint: Springer, DB - UniCat KW - Image processing KW - Computer vision. KW - Image processing. KW - Social sciences KW - Computers. KW - Education KW - Computer Imaging, Vision, Pattern Recognition and Graphics. KW - Image Processing. KW - Computer Application in Social and Behavioral Sciences. KW - Computing Milieux. KW - Computers and Education. KW - Digital techniques. KW - Data processing. KW - Aorta KW - Diagnostic imaging KW - Radiography. KW - Data processing UR - https://www.unicat.be/uniCat?func=search&query=sysid:143152797 AB - 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. ER -