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Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention

International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 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. Nicholas Heller

    1. University of Minnesota, Minneapolis, USA

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  3. Yiyu Shi

    1. University of Notre Dame, Notre Dame, USA

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  4. Yiming Xiao

    1. Western University, London, Canada

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  5. Raphael Sznitman

    1. University of Bern, Bern, Switzerland

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  6. Veronika Cheplygina

    1. Eindhoven University of Technology, Eindhoven, The Netherlands

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  7. Diana Mateus

    1. École Centrale de Nantes, Nantes, France

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  8. Emanuele Trucco

    1. University of Dundee, Dundee, UK

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  9. X. Sharon Hu

    1. University of Notre Dame, Notre Dame, USA

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  10. Danny Chen

    1. University of Notre Dame, Notre Dame, USA

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  11. Matthieu Chabanas

    1. University of Grenoble Alpes, Grenoble, France

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  12. Hassan Rivaz

    1. Concordia University, Montréal, Canada

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  13. Ingerid Reinertsen

    1. Health Research, SINTEF Digital, Trondheim, Norway

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11851)

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

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Table of contents (16 papers)

  1. Front Matter

    Pages i-xx
    Download chapter PDF

  2. 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS 2019)

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Comparison of Active Learning Strategies Applied to Lung Nodule Segmentation in CT Scans

      • Daria Zotova, Aneta Lisowska, Owen Anderson, Vismantas Dilys, Alison O’Neil
      Pages 3-12

    3. Robust Registration of Statistical Shape Models for Unsupervised Pathology Annotation

      • Dana Rahbani, Andreas Morel-Forster, Dennis Madsen, Marcel Lüthi, Thomas Vetter
      Pages 13-21

    4. XiangyaDerm: A Clinical Image Dataset of Asian Race for Skin Disease Aided Diagnosis

      • Bin Xie, Xiaoyu He, Shuang Zhao, Yi Li, Juan Su, Xinyu Zhao et al.
      Pages 22-31

    5. Data Augmentation Based on Substituting Regional MRIs Volume Scores

      • Tuo Leng, Qingyu Zhao, Chao Yang, Zhufu Lu, Ehsan Adeli, Kilian M. Pohl
      Pages 32-41

    6. Weakly Supervised Segmentation from Extreme Points

      • Holger Roth, Ling Zhang, Dong Yang, Fausto Milletari, Ziyue Xu, Xiaosong Wang et al.
      Pages 42-50

    7. Exploring the Relationship Between Segmentation Uncertainty, Segmentation Performance and Inter-observer Variability with Probabilistic Networks

      • Elisa Chotzoglou, Bernhard Kainz
      Pages 51-60

    8. DeepIGeoS-V2: Deep Interactive Segmentation of Multiple Organs from Head and Neck Images with Lightweight CNNs

      • Wenhui Lei, Huan Wang, Ran Gu, Shichuan Zhang, Shaoting Zhang, Guotai Wang
      Pages 61-69

    9. The Role of Publicly Available Data in MICCAI Papers from 2014 to 2018

      • Nicholas Heller, Jack Rickman, Christopher Weight, Nikolaos Papanikolopoulos
      Pages 70-77

  3. First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (HAL-MICCAI 2019)

    1. Front Matter

      Pages 79-79
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    2. Hardware Acceleration of Persistent Homology Computation

      • Fan Wang, Chunhua Deng, Bo Yuan, Chao Chen
      Pages 81-88

    3. Deep Compressed Pneumonia Detection for Low-Power Embedded Devices

      • Hongjia Li, Sheng Lin, Ning Liu, Caiwen Ding, Yanzhi Wang
      Pages 89-97

    4. D3MC: A Reinforcement Learning Based Data-Driven Dyna Model Compression

      • Jiahui Guan, Ravi Soni, Dibyajyoti Pati, Gopal Avinash, V. Ratna Saripalli
      Pages 98-105

    5. An Analytical Method of Automatic Alignment for Electron Tomography

      • Shuang Wen, Guojie Luo
      Pages 106-114

    6. U-Net Fixed-Point Quantization for Medical Image Segmentation

      • MohammadHossein AskariHemmat, Sina Honari, Lucas Rouhier, Christian S. Perone, Julien Cohen-Adad, Yvon Savaria et al.
      Pages 115-124

  4. Second International Challenge on Correction of Brainshift with Intra-Operative Ultrasound (CuRIOUS 2019)

    1. Front Matter

      Pages 125-125
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    2. Registration of Ultrasound Volumes Based on Euclidean Distance Transform

      • Luca Canalini, Jan Klein, Dorothea Miller, Ron Kikinis
      Pages 127-135

    3. Landmark-Based Evaluation of a Block-Matching Registration Framework on the RESECT Pre- and Intra-operative Brain Image Data Set

      • David Drobny, Marta Ranzini, Sébastien Ourselin, Tom Vercauteren, Marc Modat
      Pages 136-144

    4. Comparing Deep Learning Strategies and Attention Mechanisms of Discrete Registration for Multimodal Image-Guided Interventions

      • In Young Ha, Mattias P. Heinrich
      Pages 145-151
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  1. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention

Keywords

About this book

This book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019.

The 8 papers presented at LABELS 2019, the 5 papers presented at HAL-MICCAI 2019, and the 3 papers presented at CuRIOUS 2019 were carefully reviewed and selected from numerous submissions. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. The HAL-MICCAI papers cover a wide set of hardware applications inmedical problems, including medical image segmentation, electron tomography, pneumonia detection, etc. The CuRIOUS papers provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their image registration methods on newly released standardized datasets of iUS-guided brain tumor resection.


Editors and Affiliations

  • University of Sydney, Sydney, Australia

    Luping Zhou

  • University of Minnesota, Minneapolis, USA

    Nicholas Heller

  • University of Notre Dame, Notre Dame, USA

    Yiyu Shi, X. Sharon Hu, Danny Chen

  • Western University, London, Canada

    Yiming Xiao

  • University of Bern, Bern, Switzerland

    Raphael Sznitman

  • Eindhoven University of Technology, Eindhoven, The Netherlands

    Veronika Cheplygina

  • École Centrale de Nantes, Nantes, France

    Diana Mateus

  • University of Dundee, Dundee, UK

    Emanuele Trucco

  • University of Grenoble Alpes, Grenoble, France

    Matthieu Chabanas

  • Concordia University, Montréal, Canada

    Hassan Rivaz

  • Health Research, SINTEF Digital, Trondheim, Norway

    Ingerid Reinertsen

Bibliographic Information

  • Book Title: Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention

  • Book Subtitle: International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings

  • Editors: Luping Zhou, Nicholas Heller, Yiyu Shi, Yiming Xiao, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, X. Sharon Hu, Danny Chen, Matthieu Chabanas, Hassan Rivaz, … Ingerid Reinertsen

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-33642-4

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Softcover ISBN: 978-3-030-33641-7Published: 21 November 2019

  • eBook ISBN: 978-3-030-33642-4Published: 20 November 2019

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XX, 154

  • Number of Illustrations: 14 b/w illustrations, 48 illustrations in colour

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, Health Informatics

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