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Machine Learning for Medical Image Reconstruction

Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

  • Florian Knoll
  • Andreas Maier
  • Daniel Rueckert
  • Jong Chul Ye
Conference proceedings MLMIR 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11905)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11905)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Deep Learning for Magnetic Resonance Imaging

    1. Front Matter
      Pages 1-1
    2. Balamurali Murugesan, S. Vijaya Raghavan, Kaushik Sarveswaran, Keerthi Ram, Mohanasankar Sivaprakasam
      Pages 3-15
    3. Tong Zhang, Laurence H. Jackson, Alena Uus, James R. Clough, Lisa Story, Mary A. Rutherford et al.
      Pages 16-24
    4. Sahar Yousefi, Lydiane Hirschler, Merlijn van der Plas, Mohamed S. Elmahdy, Hessam Sokooti, Matthias Van Osch et al.
      Pages 25-35
    5. Chaoping Zhang, Florian Dubost, Marleen de Bruijne, Stefan Klein, Dirk H. J. Poot
      Pages 36-46
    6. Guanhua Wang, Enhao Gong, Suchandrima Banerjee, John Pauly, Greg Zaharchuk
      Pages 47-57
    7. Hongxiang Lin, Matteo Figini, Ryutaro Tanno, Stefano B. Blumberg, Enrico Kaden, Godwin Ogbole et al.
      Pages 58-70
    8. Patricia M. Johnson, Matthew J. Muckley, Mary Bruno, Erich Kobler, Kerstin Hammernik, Thomas Pock et al.
      Pages 71-79
    9. Mingli Zhang, Yuhong Guo, Caiming Zhang, Jean-Baptiste Poline, Alan Evans
      Pages 80-88
  3. Deep Learning for Computed Tomography

    1. Front Matter
      Pages 89-89
    2. Akira Kudo, Yoshiro Kitamura, Yuanzhong Li, Satoshi Iizuka, Edgar Simo-Serra
      Pages 91-100
    3. Yixing Huang, Alexander Preuhs, Günter Lauritsch, Michael Manhart, Xiaolin Huang, Andreas Maier
      Pages 101-112
    4. Mayank Patwari, Ralf Gutjahr, Rainer Raupach, Andreas Maier
      Pages 113-124
    5. Tristan M. Gottschalk, Björn W. Kreher, Holger Kunze, Andreas Maier
      Pages 125-136
  4. Deep Learning for General Image Reconstruction

    1. Front Matter
      Pages 137-137
    2. Benjamin Hou, Athanasios Vlontzos, Amir Alansary, Daniel Rueckert, Bernhard Kainz
      Pages 139-150
    3. Ozan Öktem, Camille Pouchol, Olivier Verdier
      Pages 151-162
    4. Shaojin Cai, Yuyang Xue, Qinquan Gao, Min Du, Gang Chen, Hejun Zhang et al.
      Pages 163-172
    5. Laura Dal Toso, Elisabeth Pfaehler, Ronald Boellaard, Julia A. Schnabel, Paul K. Marsden
      Pages 181-192
    6. Jiahong Ouyang, Guanhua Wang, Enhao Gong, Kevin Chen, John Pauly, Greg Zaharchuk
      Pages 193-204
    7. Peter A. von Niederhäusern, Carlo Seppi, Simon Pezold, Guillaume Nicolas, Spyridon Gkoumas, Stephan K. Haerle et al.
      Pages 205-214
    8. Michael Green, Miri Sklair-Levy, Nahum Kiryati, Eli Konen, Arnaldo Mayer
      Pages 215-225
    9. Alberto Gomez, Veronika Zimmer, Nicolas Toussaint, Robert Wright, James R. Clough, Bishesh Khanal et al.
      Pages 226-235
    10. Saeed Izadi, Darren Sutton, Ghassan Hamarneh
      Pages 236-244
    11. Chengjia Wang, Giorgos Papanastasiou, Sotirios Tsaftaris, Guang Yang, Calum Gray, David Newby et al.
      Pages 245-254
  5. Peter A. von Niederhäusern, Carlo Seppi, Simon Pezold, Guillaume Nicolas, Spyridon Gkoumas, Stephan K. Haerle et al.
    Pages C1-C1
  6. Back Matter
    Pages 265-266

About these proceedings

Introduction

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.

Keywords

artificial intelligence bioinformatics computer vision deep learning image analysis image processing image quality image reconstruction image segmentation imaging systems machine learning medical images medical sciences medical technologies neural networks reconstruction signal processing

Editors and affiliations

  1. 1.New York UniversityNew YorkUSA
  2. 2.University of Erlangen-NurembergErlangenGermany
  3. 3.Imperial College LondonLondonUK
  4. 4.Korea Advanced Institute of Science and TechnologyDaejeonKorea (Republic of)

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-33843-5
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-33842-8
  • Online ISBN 978-3-030-33843-5
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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