Overview

This book constitutes the First Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge, MyoPS 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 crisis.The 12 full and 4 short papers presented in this volume were carefully reviewed and selected form numerous submissions. This challenge aims not only to benchmark various myocardial pathology segmentation algorithms, but also to cover the topic of general cardiac image segmentation, registration and modeling, and raise discussions for further technical development and clinical deployment.

Full Product Details

Author:   Xiahai Zhuang ,  Lei Li
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   12554
Weight:   0.454kg
ISBN:  

9783030656508

ISBN 10:   3030656500
Pages:   177
Publication Date:   19 December 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Stacked BCDU-net with semantic CMR synthesis: application to Myocardial PathologySegmentation challenge.- EfficientSeg: A Simple but Efficient Solution to Myocardial Pathology Segmentation Challenge.- Two-stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance.- Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images.- Myocardial Edema and Scar Segmentation using a Coarse-to-Fine Framework with Weighted Ensemble.- Exploring ensemble applications for multi-sequence myocardial pathology segmentation.- Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling.- Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences.- CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-shaped Network.- Automatic Myocardial Scar Segmentation from Multi-Sequence Cardiac MRI using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module.- Dual Attention U-net for Multi-Sequence Cardiac MR Images Segmentation.- Accurate Myocardial Pathology Segmentation with Residual U-Net.- Stacked and Parallel U-Nets with Multi-Output for Myocardial Pathology Segmentation.- Dual-path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR.- Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation.- CMRadjustNet: Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks.

Reviews

Author Information

Tab Content 6

Author Website:  

Customer Reviews

Recent Reviews

No review item found!

Add your own review!

Countries Available

All regions