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OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging

Second International Workshop, OR 2.0 2019, and Second International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Luping Zhou

    1. University of Sydney, Sydney, Australia

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  2. Duygu Sarikaya

    1. University of Rennes 1, Rennes, France

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  3. Seyed Mostafa Kia

    1. Radboud University Medical Center, Nijmegen, The Netherlands

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  4. Stefanie Speidel

    1. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

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  5. Anand Malpani

    1. Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, USA

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  6. Daniel Hashimoto

    1. Harvard Medical School, Massachusetts General Hospital, Boston, USA

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  7. Mohamad Habes

    1. University of Pennsylvania, Philadelphia, USA

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  8. Tommy Löfstedt

    1. Umeå University, Umeå, Sweden

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  9. Kerstin Ritter

    1. Charité-Universitätsmedizin Berlin, Berlin, Germany

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  10. Hongzhi Wang

    1. IBM Research - Almaden, San Jose, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11796)

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

Included in the following conference series:

Conference proceedings info: MLCN 2019, OR 2.0 2019.

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Table of contents (12 papers)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Proceedings of the 2nd International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0 2019)

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Feature Aggregation Decoder for Segmenting Laparoscopic Scenes

      • Abdolrahim Kadkhodamohammadi, Imanol Luengo, Santiago Barbarisi, Hinde Taleb, Evangello Flouty, Danail Stoyanov
      Pages 3-11

    3. Preoperative Planning for Guidewires Employing Shape-Regularized Segmentation and Optimized Trajectories

      • Johannes Fauser, Moritz Fuchs, Ahmed Ghazy, Bernhard Dorweiler, Anirban Mukhopadhyay
      Pages 12-20

    4. Guided Unsupervised Desmoking of Laparoscopic Images Using Cycle-Desmoke

      • V. Vishal, Neeraj Sharma, Munendra Singh
      Pages 21-28

    5. Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration

      • Dominik Rivoir, Sebastian Bodenstedt, Felix von Bechtolsheim, Marius Distler, Jürgen Weitz, Stefanie Speidel
      Pages 29-37

    6. Live Monitoring of Haemodynamic Changes with Multispectral Image Analysis

      • Leonardo A. Ayala, Sebastian J. Wirkert, Janek Gröhl, Mildred A. Herrera, Adrian Hernandez-Aguilera, Anant Vemuri et al.
      Pages 38-46

    7. Towards a Cyber-Physical Systems Based Operating Room of the Future

      • Chin-Boon Chng, Pooi-Mun Wong, Nicholas Ho, Xiaoyu Tan, Chee-Kong Chui
      Pages 47-55

  3. Proceedings of the 2nd International Workshop on Machine Learning in Clinical Neuroimaging: Entering the Era of Big Data via Transfer Learning and Data Harmonization (MLCN 2019)

    1. Front Matter

      Pages 57-57
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    2. Deep Transfer Learning for Whole-Brain FMRI Analyses

      • Armin W. Thomas, Klaus-Robert Müller, Wojciech Samek
      Pages 59-67

    3. Knowledge Distillation for Semi-supervised Domain Adaptation

      • Mauricio Orbes-Arteainst, Jorge Cardoso, Lauge Sørensen, Christian Igel, Sebastien Ourselin, Marc Modat et al.
      Pages 68-76

    4. Relevance Vector Machines for Harmonization of MRI Brain Volumes Using Image Descriptors

      • Maria Ines Meyer, Ezequiel de la Rosa, Koen Van Leemput, Diana M. Sima
      Pages 77-85

    5. Data Pooling and Sampling of Heterogeneous Image Data for White Matter Hyperintensity Segmentation

      • Annika Hänsch, Bastian Cheng, Benedikt Frey, Carola Mayer, Marvin Petersen, Iris Lettow et al.
      Pages 86-94

    6. A Hybrid 3DCNN and 3DC-LSTM Based Model for 4D Spatio-Temporal fMRI Data: An ABIDE Autism Classification Study

      • Ahmed El-Gazzar, Mirjam Quaak, Leonardo Cerliani, Peter Bloem, Guido van Wingen, Rajat Mani Thomas
      Pages 95-102

    7. Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI Across Sites

      • Florian Dubost, Max Dünnwald, Denver Huff, Vincent Scheumann, Frank Schreiber, Meike Vernooij et al.
      Pages 103-111

  4. Back Matter

    Pages 113-114
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  1. OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging

Keywords

About this book

This book constitutes the refereed proceedings of the Second International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019, and the Second International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

For OR 2.0 all 6 submissions were accepted for publication. They aim to highlight the potential use of machine vision and perception, robotics, surgical simulation and modeling, multi-modal data fusion and visualization, image analysis, advanced imaging, advanced display technologies, human-computer interfaces, sensors, wearable and implantable electronics and robots, visual attention models, cognitive models, decision support networks to enhance surgical procedural assistance, context-awareness and team communication in the operating theater, human-robot collaborative systems, and surgical training and assessment.

MLCN 2019 accepted 6 papers out of 7 submissions for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience.

 

Editors and Affiliations

  • University of Sydney, Sydney, Australia

    Luping Zhou

  • University of Rennes 1, Rennes, France

    Duygu Sarikaya

  • Radboud University Medical Center, Nijmegen, The Netherlands

    Seyed Mostafa Kia

  • National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  • Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, USA

    Anand Malpani

  • Harvard Medical School, Massachusetts General Hospital, Boston, USA

    Daniel Hashimoto

  • University of Pennsylvania, Philadelphia, USA

    Mohamad Habes

  • Umeå University, Umeå, Sweden

    Tommy Löfstedt

  • Charité-Universitätsmedizin Berlin, Berlin, Germany

    Kerstin Ritter

  • IBM Research - Almaden, San Jose, USA

    Hongzhi Wang

Bibliographic Information

  • Book Title: OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging

  • Book Subtitle: Second International Workshop, OR 2.0 2019, and Second International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings

  • Editors: Luping Zhou, Duygu Sarikaya, Seyed Mostafa Kia, Stefanie Speidel, Anand Malpani, Daniel Hashimoto, Mohamad Habes, Tommy Löfstedt, Kerstin Ritter, … Hongzhi Wang

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-32695-1

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Softcover ISBN: 978-3-030-32694-4Published: 11 October 2019

  • eBook ISBN: 978-3-030-32695-1Published: 10 October 2019

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVI, 114

  • Number of Illustrations: 2 b/w illustrations, 33 illustrations in colour

  • Topics: Image Processing and Computer Vision, Artificial Intelligence

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