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 / edited by Antonio Pepe, Gian Marco Melito, Jan Egger.

Format:

Book

Contributor:

Pepe, Antonio, editor.

Melito, Gian Marco, editor.

Egger, Jan, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 14539

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Social sciences--Data processing.

Social sciences.

Computers.

Education--Data processing.

Education.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Image Processing.

Computer Application in Social and Behavioral Sciences.

Computing Milieux.

Computers and Education.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Image Processing.

Computer Application in Social and Behavioral Sciences.

Computing Milieux.

Computers and Education.

Physical Description:

1 online resource (154 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

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.

Contents:

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.

ISBN:

3-031-53241-4