Overview

This book constitutes the First Cerebral Aneurysm Detection Challenge, CADA 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in October 2020. The conference was planned to take place in Lima, Peru, and took place virtually due to the COVID-19 pandemic. The 9 regular papers presented in this volume, together with an overview and one introduction paper, were carefully reviewed and selected for inclusion in the book. The papers were organized in topical sections as follows: cerebral aneurysm detection; cerebral aneurysm segmentation; and cerebral aneurysm rupture risk estimation.

Full Product Details

Author:   Anja Hennemuth ,  Leonid Goubergrits ,  Matthias Ivantsits ,  Jan-Martin Kuhnigk
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2021
Volume:   12643
Weight:   0.454kg
ISBN:  

9783030728618

ISBN 10:   3030728617
Pages:   113
Publication Date:   16 April 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

Overview of the CADA Challenge at MICCAI 2020.- Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA).- Introduction.- CADA: Clinical Background and Motivation.- Cerebral Aneurysm Detection.- Deep Learning-Based 3D U-Net Cerebral Aneurysm Detection.- Detect and Identify Aneurysms Based on Ajusted 3D Attention Unet.- Cerebral Aneurysm Segmentation.- A$\nu$-net: Automatic Detection and Segmentation of Aneurysm.- 3D Attention U-Net with pretraining: A Solution to CADA-Aneurysm Segmentation Challenge.- Exploring Large Context for Cerebral Aneurysm Segmentation.- Cerebral Aneurysm Rupture Risk Estimation.- CADA Challenge: Rupture risk assessment using Computational Fluid Dynamics.- Cerebral Aneurysm Rupture Risk Estimation Using XGBoost and Fully Connected Neural Network.- Intracranial aneurysm rupture risk estimation utilizing vessel-graphs and machine learning.- Intracranial aneurysm rupture prediction with computational fluid dynamics point clouds.

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