Hugendubel.info - Die B2B Online-Buchhandlung 

Merkliste
Die Merkliste ist leer.
Bitte warten - die Druckansicht der Seite wird vorbereitet.
Der Druckdialog öffnet sich, sobald die Seite vollständig geladen wurde.
Sollte die Druckvorschau unvollständig sein, bitte schliessen und "Erneut drucken" wählen.

Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Mult

First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
BuchKartoniert, Paperback
142 Seiten
Englisch
Springererschienen am10.02.20241st ed. 2024
This book constitutes the First Segmentation of the Aorta Challenge, SEG.A. 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.mehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR53,49
E-BookPDF1 - PDF WatermarkE-Book
EUR53,49

Produkt

KlappentextThis book constitutes the First Segmentation of the Aorta Challenge, SEG.A. 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.
Details
ISBN/GTIN978-3-031-53240-5
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2024
Erscheinungsdatum10.02.2024
Auflage1st ed. 2024
Seiten142 Seiten
SpracheEnglisch
IllustrationenXII, 142 p. 74 illus., 67 illus. in color.
Artikel-Nr.55812719

Inhalt/Kritik

Inhaltsverzeichnis
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.mehr

Schlagworte