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© 2021

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

  • Esther Puyol Anton
  • Mihaela Pop
  • Maxime Sermesant
  • Victor Campello
  • Alain Lalande
  • Karim Lekadir
  • Avan Suinesiaputra
  • Oscar Camara
  • Alistair Young
Conference proceedings STACOM 2020

Part of the Lecture Notes in Computer Science book series (LNCS, volume 12592)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 12592)

Table of contents

  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

About these proceedings

Introduction

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. 

Keywords

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

Editors and affiliations

  • Esther Puyol Anton
    • 1
  • Mihaela Pop
    • 2
  • Maxime Sermesant
    • 3
  • Victor Campello
    • 4
  • Alain Lalande
    • 5
  • Karim Lekadir
    • 6
  • Avan Suinesiaputra
    • 7
  • Oscar Camara
    • 8
  • Alistair Young
    • 9
  1. 1.King's CollegeLondonUK
  2. 2.University of TorontoTorontoCanada
  3. 3.InriaSophia AntipolisFrance
  4. 4.Universitat de BarcelonaBarcelonaSpain
  5. 5.Université de BourgogneDijonFrance
  6. 6.Universitat de BarcelonaBarcelonaSpain
  7. 7.University of LeedsLeedsUK
  8. 8.Universitat Pompeu FabraBarcelonaSpain
  9. 9.King's CollegeLondonUK

Bibliographic information

  • Book Title Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges
  • Book Subtitle 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers
  • Editors Esther Puyol Anton
    Mihaela Pop
    Maxime Sermesant
    Victor Campello
    Alain Lalande
    Karim Lekadir
    Avan Suinesiaputra
    Oscar Camara
    Alistair Young
  • Series Title Lecture Notes in Computer Science
  • Series Abbreviated Title Lect.Notes Computer
  • DOI https://doi.org/10.1007/978-3-030-68107-4
  • Copyright Information Springer Nature Switzerland AG 2021
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Softcover ISBN 978-3-030-68106-7
  • eBook ISBN 978-3-030-68107-4
  • Series ISSN 0302-9743
  • Series E-ISSN 1611-3349
  • Edition Number 1
  • Number of Pages XV, 417
  • Number of Illustrations 11 b/w illustrations, 165 illustrations in colour
  • Topics Image Processing and Computer Vision
    Machine Learning
    Pattern Recognition
    Computer Appl. in Social and Behavioral Sciences
    Computers and Education
  • Buy this book on publisher's site