Accepted Papers

Regular

  1. A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI
    Nick Byrne, James Clough, Giovanni Montana, Andrew King
  2. Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view
    Marta Nuñez Garcia, Nicolas Cedilnik, Shuman Jia, Hubert Cochet, Maxime Sermesant
  3. Graph convolutional regression of cardiac depolarization from sparse endocardial maps
    Felix Meister, Tiziano Passerini, Chloe Audigier, Èric Lluch, Viorel Mihalef, Hiroshi Ashikaga, Andreas Maier, Henry Halperin, Tommaso Mansi
  4. A cartesian grid representation of left atrial appendages for deep learning-based estimation of thrombogenic risk predictors
    César Acebes, Xabier Morales, Oscar Camara
  5. Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks
    Qiaoying Huang, Eric Chen, Hanchao Yu, Yimo Guo, Terrence Chen, Dimitris Metaxas, Shanhui Sun
  6. Modelling Fine-rained Cardiac Motion via Spatio-temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions
    Ping Lu, Wenjia Bai, Daniel Rueckert, Alison Noble
  7. Towards meshfree patient-specific mitral valve modeling
    Judit Ros, Oscar Camara, Uxio Hermida, Bart Bijnens, Hernán G. Morales
  8. PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps
    Thomas Grandits, Simone Pezzuto, Jolijn Lubrecht, Thomas Pock, Gernot Plank, Rolf Krause
  9. Automatic Detection of Landmarks for Fast Cardiac MR Image Registration
    Mia Mojica, Mihaela Pop, Mehran Ebrahimi
  10. Quality-aware semi-supervised learning for CMR segmentation
    Bram Ruijsink, Esther Puyol Anton, Ye Li, Wenjia Bai, Reza Razavi, Andrew King
  11. Estimation of imaging biomarker’s progression in post-infarct patients using cross-sectional data
    Marta Nuñez Garcia, Nicolas Cedilnik, Shuman Jia, Hubert Cochet, Marco Lorenzi, Maxime Sermesant
  12. PC-U Net: Learning  to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data
    Meng Ye, Qiaoying Huang, DONG YANG, Pengxiang Wu, Jingru Yi, Leon Axel, Dimitris Metaxas
  13. Shape constrained CNN for cardiac MR segmentation with simultaneous prediction of shape and pose parameters
    Sofie Tilborghs, Tom Dresselaers, Piet Claus, Jan Bogaert, Frederik Maes
  14. Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI
    Ana Lourenço, Eric Kerfoot, Connor Dibblin, Ebraham Alskaf, Mustafa Anjari, Anil Bharath, Andrew King, Henry Chubb, Teresa Correia, Marta Varela
  15. Estimation of Cardiac Valve Annuli Motion with Deep Learning
    Eric Kerfoot, Carlos Escudero King, Tefvik Ismail, David Nordsletten, Renee Miller
  16. 4D Flow Magnetic Resonance Imaging for Left Atrial Haemodynamic Characterization and Model Calibration
    Xabier Morales, Jordi Mill, Gaspar Delso, Marta Sitges, Ada Doltra, Filip Loncaric, Bart Bijnens, Oscar Camara
  17. Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning
    Axel Aguerreberry, Alain Lalande, Ezequiel de la Rosa, Elmer Fernández

Multi-Centre, Multi-Vendor, Multi-Disease Cardiac Image Segmentation Challenge

  1. Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation
    Jun Ma
  2. Disentangled Representations for Domain-generalized Cardiac Segmentation
    Xiao Liu, Spyridon Thermos, Agisilaos Chartsias, Alison O’Neil, Sotirios Tsaftaris
  3. A 2-step Deep Learning method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation
    Jorge Corral Acero, Vaanathi Sundaresan, Nicola dinsdale, Mark Jenkinson, Vicente Grau
  4. Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information
    Lei Li, Veronika Zimmer, Wangbin Ding, Fuping Wu, Julia Schnabel, Xiahai Zhuang
  5. Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer
    Yao Zhang, Jiawei Yang, Feng Hou, Yang Liu, Yixin Wang, Jiang Tian, Cheng Zhong, Zhiqiang He
  6. Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation
    Cian Scannell, Amedeo Chiribiri, Mitko Veta
  7. The Effect of Data Augmentation on Robustness against Domain Shifts in cMRI Segmentations
    Peter Full, Paul Jäger, Fabian Isensee, Klaus Maier-Hein
  8. A deep convolutional neural network approach for the segmentation of cardiac structures from MRI sequences
    Adam Carscadden, Michelle Noga, Kumaradevan Punithakumar
  9. Multi-center, Multi-vendor, and Multi-DiseaseCardiac Image Segmentation Using Scale-Independent Multi-Gate UNET
    Mina Essam, Dina Abdelrauof, Mustafa Elattar
  10. Adaptive Preprocessing for Generalization in Cardiac MR Image Segmentation
    Firas Khader, Justus Schock, Daniel Truhn, Fabian Morsbach, Christoph Haarburger
  11. Deidentifying MRI data domain by iterative backpropagation
    Mario Parreño Lara, Roberto Paredes, Alberto Albiol
  12. A generalizable deep-learning approach for cardiac magnetic resonance image segmentation using image augmentation and attention U-Net
    Fanwei Kong, Shawn Shadden
  13. Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation
    Hongwei Li, Jianguo Zhang, Bjoern Menze
  14. Style-invariant Cardiac Image Segmentation with Test-time Augmentation
    Xiaoqiong Huang, Zejian Chen, Xin Yang, Wufeng Xue, Zhendong Liu, Yuxin Zou, Mingyuan Luo, Dong Ni

Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge

  1. EfficientSeg: A Simple but Efficient Solution to Myocardial Pathology Segmentation Challenge
    Jianpeng Zhang, Yutong Xie, Zhibin Liao, Johan Verjans, Yong Xia
  2. Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation
    Jun Ma
  3. Two-stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance
    Yanfei Liu, Maodan Zhang, Qi Zhan, Dongdong Gu, Guocai Liu
  4. Dual-path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR 
    Feiyan Li, Weisheng Li
  5. Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling
    Haochuan Jiang, Chengjia Wang, Agisilaos Chartsias,  Sotirios A.Tsaftaris
  6. CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-shaped Network
    Weisheng Li, Linhong Wang, Sheng Qin
  7. Dual Attention U-net for Multi-Sequence Cardiac MR Images Segmentation
    Hong Yu, Sen Zha, Yubin Huangfu, Chen Chen, Meng Ding, Jiangyun Li
  8. Automatic Myocardial Scar Segmentation from Multi-Sequence Cardiac MRI using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module
    Tewodros Weldebirhan Arega, Stephanie Bricq
  9. Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images
    Zhen Zhang, Chenyu Liu, Wangbin Ding, Sihan Wang, Chenhao Pei, Mingjing Yang, Liqin Huang
  10. Accurate Myocardial Pathology Segmentation with Residual U-Net
    Altunok Elif, Oksuz Ilkay
  11. Myocardial Edema and Scar Segmentation using a Coarse-to-Fine Framework with Weighted Ensemble
    Shuwei Zhai, Ran Gu, Wenhui Lei, Guotai Wang
  12. Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences
    Xiaoran Zhang, Michelle Noga, Kumaradevan Punithakumar
  13. Exploring ensemble applications for multi-sequence myocardial pathology segmentation
    Markus J. Ankenbrand, David Lohr, Laura M. Schreiber
  14. Stacked and Parallel U-Nets with Multi-Output for Myocardial Pathology Segmentation
    Zhou Zhao, Nicolas Boutry, ElodiePuybareau
  15. Stacked BCDU-net with semantic CMR synthesis: application to Myocardial PathologySegmentation challenge
    Carlos Mart́ın-Isla, Maryam Asadi-Aghbolaghi, Polyxeni Gkontra, Victor M. Campello, Sergio Escalera,  Karim Lekadir
  16. Recognition and correction cardiac MRI orientation via multi-tasking learning and deep neural networks
    Ke Zhang, Xiahai Zhuang

Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI Challenge

  1. Comparison of a Hybrid Mixture Model and a CNN for the Segmentation Myocardial Pathologies in Delayed  enhancement MRI
    Markus Hullebrand, Matthias Ivantsits, Hannu Zhang, Peter Kohlmann, Jan-Martin Kuhnigk, Titus Kuehne,Stefan Schonberg, Anja Hennemuth
  2. Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI
    Yichi Zhang
  3. Cascaded Framework for Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI
    Jun Ma
  4. Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information
    Ana Lourenco, Eric Kerfoot, Irina Grigorescu, Cian M Scannell, Marta Varela, Teresa M Correia
  5. SM2N2: A Stacked Architecture for Multimodal Data and its Application to Myocardial Infarction Detection
    Rishabh Sharma, Christoph F. Eick, Nikolaos V. Tsekos
  6. A Hybrid Network for Automatic Myocardial Infarction Segmentation in Delayed Enhancement-MRI
    Sen Yang, Xiyue Wang
  7. Efficient 3D deep learning for myocardial diseases segmentation
    Khawla Brahim, Abdul Qayyum, Alain Lalande, Arnaud Boucher, Anis Sakly, Fabrice Meriaudeau
  8. Deep-learning-based myocardial pathology Detection
    Matthias Ivantsits, Markus Hullebrand, Sebastian Kelle, Titus Kuehne, Anja Hennemuth
  9. Automatic myocardial infarction evaluation from DE-MRI using deep convolutional networks
    Kibrom B. Girum, Youssef Skandarani, Raabid Hussain, Alexis Bozorg-Grayeli, Gilles Crehange, Alain Lalande
  10. Uncertainty based Segmentation of Myocardial Infarction Areas on Cardiac MR images
    Thomas Crozier, Alexis Faure, Daniel Bos, Marleen De Bruijne, Robin Camarasa
  11. Anatomy Prior Based U-net for Pathology Segmentation with Attention
    Yuncheng Zhou, Ke Zhang, Xinzhe Luo, Sihan Wang, Xiahai Zhuang
  12. Automatic Scar Segmentation from DE-MRI Using 2D Dilated UNet with Rotation-based Augmentation
    Xue Feng, Christopher M. Kramer, Michael Salerno, Craig H. Meyer
  13. Classication of pathological cases of myocardial infarction using Convolutional Neural Network and Random Forest
    Jixi Shi, Zhihao Chen, Raphael Couturier