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Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges

10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Mihaela Pop

    1. University of Toronto, Toronto, Canada

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  2. Maxime Sermesant

    1. Inria, Sophia Antipolis, France

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  3. Oscar Camara

    1. Pompeu Fabra University, Barcelona, Spain

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  4. Xiahai Zhuang

    1. Fudan University, Shanghai, China

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  5. Shuo Li

    1. St Joseph’s Health Care, London, Canada

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  6. Alistair Young

    1. King’s College London, London, UK

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  7. Tommaso Mansi

    1. Siemens Medical Solutions USA, Inc., Princeton, USA

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  8. Avan Suinesiaputra

    1. University of Auckland, Auckland, New Zealand

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12009)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: STACOM 2019.

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Table of contents (42 papers)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Regular Papers

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Co-registered Cardiac ex vivo DT Images and Histological Images for Fibrosis Quantification

      • Peter Lin, Anne Martel, Susan Camilleri, Mihaela Pop
      Pages 3-11

    3. Manufacturing of Ultrasound- and MRI-Compatible Aortic Valves Using 3D Printing for Analysis and Simulation

      • Shu Wang, Harminder Gill, Weifeng Wan, Helen Tricker, Joao Filipe Fernandes, Yohan Noh et al.
      Pages 12-21

    4. Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders

      • Esther Puyol-Antón, Bram Ruijsink, James R. Clough, Ilkay Oksuz, Daniel Rueckert, Reza Razavi et al.
      Pages 22-30

    5. Ultra-DenseNet for Low-Dose X-Ray Image Denoising in Cardiac Catheter-Based Procedures

      • Yimin Luo, Daniel Toth, Kui Jiang, Kuberan Pushparajah, Kawal Rhode
      Pages 31-42

    6. A Cascade Regression Model for Anatomical Landmark Detection

      • Zimeng Tan, Yongjie Duan, Ziyi Wu, Jianjiang Feng, Jie Zhou
      Pages 43-51

    7. Comparison of 2D Echocardiography and Cardiac Cine MRI in the Assessment of Regional Left Ventricular Wall Thickness

      • Vera H. J. van Hal, Debbie Zhao, Kathleen Gilbert, Thiranja P. Babarenda Gamage, Charlene Mauger, Robert N. Doughty et al.
      Pages 52-62

    8. Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutional Neural Networks

      • Zhaohan Xiong, Aaqel Nalar, Kevin Jamart, Martin K. Stiles, Vadim V. Fedorov, Jichao Zhao
      Pages 63-71

    9. 4D CNN for Semantic Segmentation of Cardiac Volumetric Sequences

      • Andriy Myronenko, Dong Yang, Varun Buch, Daguang Xu, Alvin Ihsani, Sean Doyle et al.
      Pages 72-80

    10. Two-Stage 2D CNN for Automatic Atrial Segmentation from LGE-MRIs

      • Kevin Jamart, Zhaohan Xiong, Gonzalo Maso Talou, Martin K. Stiles, Jichao Zhao
      Pages 81-89

    11. 3D Left Ventricular Segmentation from 2D Cardiac MR Images Using Spatial Context

      • Sofie Tilborghs, Tom Dresselears, Piet Claus, Jan Bogaert, Frederik Maes
      Pages 90-99

    12. Towards Hyper-Reduction of Cardiac Models Using Poly-affine Transformations

      • Gaëtan Desrues, Hervé Delingette, Maxime Sermesant
      Pages 100-108

    13. Conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual Frames

      • Julius Ossenberg-Engels, Vicente Grau
      Pages 109-118

    14. Learning Interactions Between Cardiac Shape and Deformation: Application to Pulmonary Hypertension

      • Maxime Di Folco, Patrick Clarysse, Pamela Moceri, Nicolas Duchateau
      Pages 119-127

    15. Multimodal Cardiac Segmentation Using Disentangled Representation Learning

      • Agisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang, Colin Stirrat, Scott Semple, David Newby et al.
      Pages 128-137

    16. DeepLA: Automated Segmentation of Left Atrium from Interventional 3D Rotational Angiography Using CNN

      • Kobe Bamps, Stijn De Buck, Jeroen Bertels, Rik Willems, Christophe Garweg, Peter Haemers et al.
      Pages 138-146

    17. Non-invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-Driven or Model-Based?

      • Yingyu Yang, Stephane Gillon, Jaume Banus, Pamela Moceri, Maxime Sermesant
      Pages 147-156

    18. Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the Left Atrial Appendage

      • Xabier Morales, Jordi Mill, Kristine A. Juhl, Andy Olivares, Guillermo Jimenez-Perez, Rasmus R. Paulsen et al.
      Pages 157-166

    19. End-to-end Cardiac Ultrasound Simulation for a Better Understanding of Image Quality

      • Alexandre Legay, Thomas Tiennot, Jean-François Gelly, Maxime Sermesant, Jean Bulté
      Pages 167-175
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  1. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges

Keywords

About this book

This book constitutes the thoroughly refereed post-workshop proceedings of the 10th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

The 42 revised full workshop papers were carefully reviewed and selected from 76 submissions. The topics of the workshop included: cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, 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.

Editors and Affiliations

  • University of Toronto, Toronto, Canada

    Mihaela Pop

  • Inria, Sophia Antipolis, France

    Maxime Sermesant

  • Pompeu Fabra University, Barcelona, Spain

    Oscar Camara

  • Fudan University, Shanghai, China

    Xiahai Zhuang

  • St Joseph’s Health Care, London, Canada

    Shuo Li

  • King’s College London, London, UK

    Alistair Young

  • Siemens Medical Solutions USA, Inc., Princeton, USA

    Tommaso Mansi

  • University of Auckland, Auckland, New Zealand

    Avan Suinesiaputra

Bibliographic Information

  • Book Title: Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges

  • Book Subtitle: 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers

  • Editors: Mihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, … Avan Suinesiaputra

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-39074-7

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Softcover ISBN: 978-3-030-39073-0Published: 23 January 2020

  • eBook ISBN: 978-3-030-39074-7Published: 22 January 2020

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XV, 417

  • Number of Illustrations: 32 b/w illustrations, 168 illustrations in colour

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, Pattern Recognition, Computer Applications

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