Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges

1. Front Matter

   Pages i-xv

   PDF

2. Regular Papers

   1. Front Matter

      Pages 1-1

      PDF

   2. A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI

      • Nick Byrne, James R. Clough, Giovanni Montana, Andrew P. King

      Pages 3-13

   3. Automatic Multiplanar CT Reformatting from Trans-Axial into Left Ventricle Short-Axis View

      • Marta Nuñez-Garcia, Nicolas Cedilnik, Shuman Jia, Maxime Sermesant, Hubert Cochet

      Pages 14-22

   4. Graph Convolutional Regression of Cardiac Depolarization from Sparse Endocardial Maps

      • Felix Meister, Tiziano Passerini, Chloé Audigier, Èric Lluch, Viorel Mihalef, Hiroshi Ashikaga et al.

      Pages 23-34

   5. A Cartesian Grid Representation of Left Atrial Appendages for a Deep Learning Estimation of Thrombogenic Risk Predictors

      • César Acebes, Xabier Morales, Oscar Camara

      Pages 35-43

   6. Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks

      • Qiaoying Huang, Eric Z. Chen, Hanchao Yu, Yimo Guo, Terrence Chen, Dimitris Metaxas et al.

      Pages 44-55

   7. Modelling Cardiac Motion via Spatio-Temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions

      • Ping Lu, Wenjia Bai, Daniel Rueckert, J. Alison Noble

      Pages 56-65

   8. Towards Mesh-Free Patient-Specific Mitral Valve Modeling

      • Judit Ros, Oscar Camara, Uxio Hermida, Bart Bijnens, Hernán G. Morales

      Pages 66-75

   9. PIEMAP: Personalized Inverse Eikonal Model from Cardiac Electro-Anatomical Maps

      • Thomas Grandits, Simone Pezzuto, Jolijn M. Lubrecht, Thomas Pock, Gernot Plank, Rolf Krause

      Pages 76-86

   10. Automatic Detection of Landmarks for Fast Cardiac MR Image Registration

       • Mia Mojica, Mihaela Pop, Mehran Ebrahimi

       Pages 87-96

   11. Quality-Aware Semi-supervised Learning for CMR Segmentation

       • Bram Ruijsink, Esther Puyol-Antón, Ye Li, Wenjia Bai, Eric Kerfoot, Reza Razavi et al.

       Pages 97-107

   12. 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

       Pages 108-116

   13. 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 et al.

       Pages 117-126

   14. 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

       Pages 127-136

   15. 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 A. Bharath et al.

       Pages 137-145

   16. Estimation of Cardiac Valve Annuli Motion with Deep Learning

       • Eric Kerfoot, Carlos Escudero King, Tefvik Ismail, David Nordsletten, Renee Miller

       Pages 146-155

   17. 4D Flow Magnetic Resonance Imaging for Left Atrial Haemodynamic Characterization and Model Calibration

       • Xabier Morales, Jordi Mill, Gaspar Delso, Filip Loncaric, Ada Doltra, Xavier Freixa et al.

       Pages 156-165

   18. Segmentation-Free Estimation of Aortic Diameters from MRI Using Deep Learning

       • Axel Aguerreberry, Ezequiel de la Rosa, Alain Lalande, Elmer Fernández

       Pages 166-174

3. Multi-centre, Multi-vendor, Multi-disease Cardiac Image Segmentation Challenge (M&Ms)

   1. Front Matter

      Pages 175-175

      PDF

This book constitutes the proceedings of the 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020, as well as two challenges: M&Ms - The Multi-Centre, Multi-Vendor, Multi-Disease Segmentation Challenge, and EMIDEC - Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI Challenge. The 43 full papers included in this volume were carefully reviewed and selected from 70 submissions. They deal with cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, artificial intelligence, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods. 

• artificial intelligence
• cardiac imaging
• computer modelling
• computer vision
• CT
• deep learning
• electro-mechanical heart models
• image analysis
• image processing
• image reconstruction
• machine learning
• medical images
• MRI
• network protocols
• neural networks
• object recognition
• pattern recognition
• signal processing

• King's College, London, UK

  Esther Puyol Anton, Alistair Young

• University of Toronto, Toronto, Canada

  Mihaela Pop

• Inria, Sophia Antipolis, France

  Maxime Sermesant

• Universitat de Barcelona, Barcelona, Spain

  Victor Campello, Karim Lekadir

• Université de Bourgogne, Dijon, France

  Alain Lalande

• University of Leeds, Leeds, UK

  Avan Suinesiaputra

• Universitat Pompeu Fabra, Barcelona, Spain

  Oscar Camara

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