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M&Ms and EMIDEC/STACOM@MICCAI 2020: Lima, Peru
- Esther Puyol-Antón, Mihaela Pop, Maxime Sermesant, Víctor M. Campello, Alain Lalande, Karim Lekadir
, Avan Suinesiaputra, Oscar Camara, Alistair A. Young:
Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers. Lecture Notes in Computer Science 12592, Springer 2021, ISBN 978-3-030-68106-7
Regular Papers
- Nick Byrne
A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI. 3-13 - Marta Nuñez Garcia
Automatic Multiplanar CT Reformatting from Trans-Axial into Left Ventricle Short-Axis View. 14-22 - Felix Meister, Tiziano Passerini, Chloé Audigier, Èric Lluch, Viorel Mihalef, Hiroshi Ashikaga, Andreas Maier, Henry R. Halperin, Tommaso Mansi:
Graph Convolutional Regression of Cardiac Depolarization from Sparse Endocardial Maps. 23-34 - César Acebes
A Cartesian Grid Representation of Left Atrial Appendages for a Deep Learning Estimation of Thrombogenic Risk Predictors. 35-43 - Qiaoying Huang, Eric Z. Chen, Hanchao Yu, Yimo Guo, Terrence Chen, Dimitris N. Metaxas, Shanhui Sun:
Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks. 44-55 - Ping Lu
Modelling Cardiac Motion via Spatio-Temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions. 56-65 - Judit Ros, Oscar Camara, Uxio Hermida
Towards Mesh-Free Patient-Specific Mitral Valve Modeling. 66-75 - Thomas Grandits
PIEMAP: Personalized Inverse Eikonal Model from Cardiac Electro-Anatomical Maps. 76-86 - Mia Mojica, Mihaela Pop, Mehran Ebrahimi
Automatic Detection of Landmarks for Fast Cardiac MR Image Registration. 87-96 - Bram Ruijsink, Esther Puyol-Antón, Ye Li, Wenjia Bai, Eric Kerfoot, Reza Razavi, Andrew P. King:
Quality-Aware Semi-supervised Learning for CMR Segmentation. 97-107 - Marta Nuñez Garcia
Estimation of Imaging Biomarker's Progression in Post-infarct Patients Using Cross-sectional Data. 108-116 - Meng Ye, Qiaoying Huang, Dong Yang, Pengxiang Wu, Jingru Yi, Leon Axel, Dimitris N. Metaxas:
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data. 117-126 - Sofie Tilborghs
Shape Constrained CNN for Cardiac MR Segmentation with Simultaneous Prediction of Shape and Pose Parameters. 127-136 - Ana Lourenço, Eric Kerfoot
Left Atrial Ejection Fraction Estimation Using SEGANet for Fully Automated Segmentation of CINE MRI. 137-145 - Eric Kerfoot
Estimation of Cardiac Valve Annuli Motion with Deep Learning. 146-155 - Xabier Morales, Jordi Mill, Gaspar Delso, Filip Loncaric, Ada Doltra, Xavier Freixa, Marta Sitges, Bart H. Bijnens, Oscar Camara:
4D Flow Magnetic Resonance Imaging for Left Atrial Haemodynamic Characterization and Model Calibration. 156-165 - Axel Aguerreberry, Ezequiel de la Rosa, Alain Lalande, Elmer Fernandez:
Segmentation-Free Estimation of Aortic Diameters from MRI Using Deep Learning. 166-174
Multi-centre, Multi-vendor, Multi-disease Cardiac Image Segmentation Challenge (M&Ms)
- Jun Ma
Histogram Matching Augmentation for Domain Adaptation with Application to Multi-centre, Multi-vendor and Multi-disease Cardiac Image Segmentation. 177-186 - Xiao Liu, Spyridon Thermos, Agisilaos Chartsias, Alison O'Neil, Sotirios A. Tsaftaris:
Disentangled Representations for Domain-Generalized Cardiac Segmentation. 187-195 - Jorge Corral Acero
A 2-Step Deep Learning Method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation. 196-207 - Lei Li
Random Style Transfer Based Domain Generalization Networks Integrating Shape and Spatial Information. 208-218 - Yao Zhang, Jiawei Yang, Feng Hou, Yang Liu, Yixin Wang, Jiang Tian, Cheng Zhong, Yang Zhang, Zhiqiang He:
Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer. 219-227 - Cian M. Scannell
Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation. 228-237 - Peter M. Full
Studying Robustness of Semantic Segmentation Under Domain Shift in Cardiac MRI. 238-249 - Adam Carscadden, Michelle Noga
A Deep Convolutional Neural Network Approach for the Segmentation of Cardiac Structures from MRI Sequences. 250-258 - Mina Saber, Dina Abdelrauof, Mustafa Elattar:
Multi-center, Multi-vendor, and Multi-disease Cardiac Image Segmentation Using Scale-Independent Multi-gate UNET. 259-268 - Firas Khader, Justus Schock, Daniel Truhn, Fabian Morsbach, Christoph Haarburger:
Adaptive Preprocessing for Generalization in Cardiac MR Image Segmentation. 269-276 - Mario Parreño, Roberto Paredes, Alberto Albiol:
Deidentifying MRI Data Domain by Iterative Backpropagation. 277-286 - Fanwei Kong
A Generalizable Deep-Learning Approach for Cardiac Magnetic Resonance Image Segmentation Using Image Augmentation and Attention U-Net. 287-296 - Hongwei Li, Jianguo Zhang
Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation. 297-304 - Xiaoqiong Huang, Zejian Chen, Xin Yang, Zhendong Liu, Yuxin Zou, Mingyuan Luo, Wufeng Xue, Dong Ni:
Style-Invariant Cardiac Image Segmentation with Test-Time Augmentation. 305-315
Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI Challenge (EMIDEC)
- Markus Hüllebrand
Comparison of a Hybrid Mixture Model and a CNN for the Segmentation of Myocardial Pathologies in Delayed Enhancement MRI. 319-327 - Yichi Zhang
Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI. 328-333 - Ana Lourenço, Eric Kerfoot
Automatic Myocardial Disease Prediction from Delayed-Enhancement Cardiac MRI and Clinical Information. 334-341 - Rishabh Sharma
SM2N2: A Stacked Architecture for Multimodal Data and Its Application to Myocardial Infarction Detection. 342-350 - Sen Yang, Xiyue Wang
A Hybrid Network for Automatic Myocardial Infarction Segmentation in Delayed Enhancement-MRI. 351-358 - Khawla Brahim, Abdul Qayyum, Alain Lalande, Arnaud Boucher, Anis Sakly, Fabrice Mériaudeau:
Efficient 3D Deep Learning for Myocardial Diseases Segmentation. 359-368 - Matthias Ivantsits
Deep-Learning-Based Myocardial Pathology Detection. 369-377 - Kibrom Berihu Girum, Youssef Skandarani, Raabid Hussain, Alexis Bozorg Grayeli, Gilles Créhange, Alain Lalande:
Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI Using Deep Convolutional Networks. 378-384 - Robin Camarasa, Alexis Faure, Thomas Crozier, Daniel Bos
Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images. 385-391 - Yuncheng Zhou, Ke Zhang, Xinzhe Luo, Sihan Wang, Xiahai Zhuang:
Anatomy Prior Based U-net for Pathology Segmentation with Attention. 392-399 - Xue Feng
Automatic Scar Segmentation from DE-MRI Using 2D Dilated UNet with Rotation-Based Augmentation. 400-405 - Jixi Shi, Zhihao Chen, Raphaël Couturier:
Classification of Pathological Cases of Myocardial Infarction Using Convolutional Neural Network and Random Forest. 406-413
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