Del 12554 i serien Lecture Notes in Computer Science

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

Häftad, Engelska, 2020

709 kr

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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.

Produktinformation

Utgivningsdatum2020-12-19
Mått155 x 235 x 11 mm
Vikt295 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor177
FörlagSpringer Nature Switzerland AG
ISBN9783030656508

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Tillämpad datateknik inom Data och IT
Systemvetenskap och AI inom Data och IT
Biovetenskap inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT

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.