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Machine Learning for Medical Image Reconstruction

First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings

  • Florian Knoll
  • Andreas Maier
  • Daniel Rueckert
Conference proceedings MLMIR 2018

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11074)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11074)

Table of contents

  1. Front Matter
    Pages I-X
  2. Deep Learning for Magnetic Resonance Imaging

    1. Front Matter
      Pages 1-1
    2. Akshay Chaudhari, Zhongnan Fang, Jin Hyung Lee, Garry Gold, Brian Hargreaves
      Pages 3-11
    3. Changheun Oh, Dongchan Kim, Jun-Young Chung, Yeji Han, HyunWook Park
      Pages 12-20
    4. Ilkay Oksuz, James Clough, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Rene Botnar et al.
      Pages 21-29
    5. Muneer Ahmad Dedmari, Sailesh Conjeti, Santiago Estrada, Phillip Ehses, Tony Stöcker, Martin Reuter
      Pages 30-38
    6. Fabian Balsiger, Amaresha Shridhar Konar, Shivaprasad Chikop, Vimal Chandran, Olivier Scheidegger, Sairam Geethanath et al.
      Pages 39-46
    7. Eunju Cha, Eung Yeop Kim, Jong Chul Ye
      Pages 47-54
    8. Chen Qin, Wenjia Bai, Jo Schlemper, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer et al.
      Pages 55-63
    9. Jo Schlemper, Daniel C. Castro, Wenjia Bai, Chen Qin, Ozan Oktay, Jinming Duan et al.
      Pages 64-71
  3. Deep Learning for Computed Tomography

    1. Front Matter
      Pages 73-73
    2. Franz Thaler, Kerstin Hammernik, Christian Payer, Martin Urschler, Darko Štern
      Pages 75-82
    3. Bastian Bier, Katharina Aschoff, Christopher Syben, Mathias Unberath, Marc Levenston, Garry Gold et al.
      Pages 83-90
    4. Andreas Kofler, Markus Haltmeier, Christoph Kolbitsch, Marc Kachelrieß, Marc Dewey
      Pages 91-99
  4. Deep Learning for General Image Reconstruction

    1. Front Matter
      Pages 101-101
    2. Andreas Hauptmann, Ben Cox, Felix Lucka, Nam Huynh, Marta Betcke, Paul Beard et al.
      Pages 103-111
    3. Hanene Ben Yedder, Aïcha BenTaieb, Majid Shokoufi, Amir Zahiremami, Farid Golnaraghi, Ghassan Hamarneh
      Pages 112-119
    4. Valery Vishnevskiy, Sergio J. Sanabria, Orcun Goksel
      Pages 120-128
    5. Felix Horger, Tobias Würfl, Vincent Christlein, Andreas Maier
      Pages 129-137
    6. Alon Baram, Moshe Safran, Avi Ben-Cohen, Hayit Greenspan
      Pages 138-146
    7. Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov, Alex Bronstein, Michael Zibulevsky, Oleg Michailovich et al.
      Pages 147-155
  5. Back Matter
    Pages 157-158

About these proceedings

Introduction

This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018.

The 17 full papers presented were carefully reviewed and selected from 21 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 Computer architecture Computerized tomography Image analysis Image processing Image quality Image reconstruction Image segmentation Life and medical sciences Machine learning Machine learning algorithms Machine learning theory Medical imaging Medical technologies Neural networks Reconstruction Sensors

Editors and affiliations

  1. 1.New York UniversityNew YorkUSA
  2. 2.University of Erlangen-NurembergErlangenGermany
  3. 3.Imperial College LondonLondonUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-00129-2
  • Copyright Information Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-00128-5
  • Online ISBN 978-3-030-00129-2
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site