Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images : First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / edited by Xiahai Zhuang, Lei Li.

Format:

Book

Contributor:

Zhuang, Xiahai, Editor.

Li, Lei, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12554

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12554

Language:

English

Subjects (All):

Image processing-Digital techniques.

Computer vision.

Artificial intelligence.

Social sciences-Data processing.

Bioinformatics.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computer Application in Social and Behavioral Sciences.

Computational and Systems Biology.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computer Application in Social and Behavioral Sciences.

Computational and Systems Biology.

Physical Description:

1 online resource (VIII, 177 pages) : 91 illustrations, 77 illustrations in color.

Edition:

1st ed. 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

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.

Contents:

Stacked binary coded decimalU-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.

Other Format:

Printed edition:

ISBN:

978-3-030-65651-5

9783030656515

Access Restriction:

Restricted for use by site license.