Segmentation of the Aorta: First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Vancouver,...

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Antonio Pepe Gian Marco Melito

Segmentation of the Aorta

Name: Segmentation of the Aorta
Price: 101.37 AUD
Availability: OutOfStock
ISBN: 9783031532405

First Challenge, SEG.A. 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October...

Antonio Pepe, Gian Marco Melito, Jan Egger

$126.95 $101.37

Paperback

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English

Springer International Publishing AG
11 March 2024

Educational equipment & technology, computer-aided learning (CAL); Imaging systems & technology; Computing: general; Computer vision

Series: Lecture Notes in Computer Science

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.

Edited by: Antonio Pepe, Gian Marco Melito, Jan Egger
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Edition: 1st ed. 2024
Volume: 14539
Dimensions: Height: 235mm, Width: 155mm,
ISBN: 9783031532405
ISBN 10: 3031532406
Series: Lecture Notes in Computer Science
Publication Date: 11 March 2024
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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.