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Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings

  • Conference proceedings
  • © 2018

Overview

Editors:
  1. Andrew Melbourne

    1. University College London, London, UK

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  2. Roxane Licandro

    1. TU Wien, Vienna, Austria

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  3. Matthew DiFranco

    1. University of California, San Francisco, USA

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  4. Paolo Rota

    1. Italian Institute of Technology, Genoa, Italy

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  5. Melanie Gau

    1. TU Wien, Vienna, Austria

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  6. Martin Kampel

    1. TU Wien, Vienna, Austria

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  7. Rosalind Aughwane

    1. University College London, London, UK

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  8. Pim Moeskops

    1. University Medical Center Utrecht, Utrecht, The Netherlands

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  9. Ernst Schwartz

    1. Medical University of Vienna, Vienna, Austria

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  10. Emma Robinson

    1. King's College London, London, UK

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  11. Antonios Makropoulos

    1. Imperial College London, London, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11076)

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

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About this book

This book constitutes the refereed joint proceedings of the First International Workshop on Data Driven Treatment Response Assessment, DATRA 2018 and the Third International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.
The 5 full papers presented at DATRA 2018 and the 12 full papers presented at PIPPI 2018 were carefully reviewed and selected. 
The DATRA  papers cover a wide range of exploring pattern recognition technologies for tackling clinical issues related to the follow-up analysis of medical data with focus on malignancy progression analysis, computer-aided models of treatment response, and anomaly detection in recovery feedback.
The PIPPI papers cover topics of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infantand paediatric period.

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Table of contents (17 papers)

  1. Front Matter

    Pages I-XI
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  2. DATRA

    1. Front Matter

      Pages 1-1
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    2. DeepCS: Deep Convolutional Neural Network and SVM Based Single Image Super-Resolution

      • Jebaveerasingh Jebadurai, J. Dinesh Peter
      Pages 3-13

    3. Automatic Segmentation of Thigh Muscle in Longitudinal 3D T1-Weighted Magnetic Resonance (MR) Images

      • Zihao Tang, Chenyu Wang, Phu Hoang, Sidong Liu, Weidong Cai, Domenic Soligo et al.
      Pages 14-21

    4. Detecting Bone Lesions in Multiple Myeloma Patients Using Transfer Learning

      • Matthias Perkonigg, Johannes Hofmanninger, Björn Menze, Marc-André Weber, Georg Langs
      Pages 22-30

    5. Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response

      • Sadegh Riyahi, Wookjin Choi, Chia-Ju Liu, Saad Nadeem, Shan Tan, Hualiang Zhong et al.
      Pages 31-41

    6. Optimizing External Surface Sensor Locations for Respiratory Tumor Motion Prediction

      • Yusuf Özbek, Zoltan Bardosi, Srdjan Milosavljevic, Wolfgang Freysinger
      Pages 42-51

  3. PIPPI

    1. Front Matter

      Pages 53-53
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    2. Segmentation of Fetal Adipose Tissue Using Efficient CNNs for Portable Ultrasound

      • Sagar Vaze, Ana I. L. Namburete
      Pages 55-65

    3. Automatic Shadow Detection in 2D Ultrasound Images

      • Qingjie Meng, Christian Baumgartner, Matthew Sinclair, James Housden, Martin Rajchl, Alberto Gomez et al.
      Pages 66-75

    4. Multi-channel Groupwise Registration to Construct an Ultrasound-Specific Fetal Brain Atlas

      • Ana I. L. Namburete, Raquel van Kampen, Aris T. Papageorghiou, Bartłomiej W. Papież
      Pages 76-86

    5. Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach

      • Susmita Saha, Alex Pagnozzi, Joanne George, Paul B. Colditz, Roslyn Boyd, Stephen Rose et al.
      Pages 87-96

    6. Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach

      • A. Virzì, P. Gori, C. O. Muller, E. Mille, Q. Peyrot, L. Berteloot et al.
      Pages 97-106

    7. Multi-view Image Reconstruction: Application to Fetal Ultrasound Compounding

      • Veronika A. Zimmer, Alberto Gomez, Yohan Noh, Nicolas Toussaint, Bishesh Khanal, Robert Wright et al.
      Pages 107-116

    8. EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging Without External Trackers

      • Bishesh Khanal, Alberto Gomez, Nicolas Toussaint, Steven McDonagh, Veronika Zimmer, Emily Skelton et al.
      Pages 117-127

    9. Better Feature Matching for Placental Panorama Construction

      • Praneeth Sadda, John A. Onofrey, Mert O. Bahtiyar, Xenophon Papademetris
      Pages 128-137

    10. Combining Deep Learning and Multi-atlas Label Fusion for Automated Placenta Segmentation from 3DUS

      • Baris U. Oguz, Jiancong Wang, Natalie Yushkevich, Alison Pouch, James Gee, Paul A. Yushkevich et al.
      Pages 138-148

    11. LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images

      • Robert Wright, Bishesh Khanal, Alberto Gomez, Emily Skelton, Jacqueline Matthew, Jo V. Hajnal et al.
      Pages 149-159

    12. Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-scale Dense Networks

      • Peiyao Kong, Dong Ni, Siping Chen, Shengli Li, Tianfu Wang, Baiying Lei
      Pages 160-168

    13. Paediatric Liver Segmentation for Low-Contrast CT Images

      • Mariusz Bajger, Gobert Lee, Martin Caon
      Pages 169-178
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Other volumes

  1. Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

Editors and Affiliations

  • University College London, London, UK

    Andrew Melbourne, Rosalind Aughwane

  • TU Wien, Vienna, Austria

    Roxane Licandro, Melanie Gau, Martin Kampel

  • University of California, San Francisco, USA

    Matthew DiFranco

  • Italian Institute of Technology, Genoa, Italy

    Paolo Rota

  • University Medical Center Utrecht, Utrecht, The Netherlands

    Pim Moeskops

  • Medical University of Vienna, Vienna, Austria

    Ernst Schwartz

  • King's College London, London, UK

    Emma Robinson

  • Imperial College London, London, UK

    Antonios Makropoulos

Bibliographic Information

  • Book Title: Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

  • Book Subtitle: First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings

  • Editors: Andrew Melbourne, Roxane Licandro, Matthew DiFranco, Paolo Rota, Melanie Gau, Martin Kampel, Rosalind Aughwane, Pim Moeskops, Ernst Schwartz, Emma Robinson, … Antonios Makropoulos

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-00807-9

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2018

  • Softcover ISBN: 978-3-030-00806-2Published: 15 September 2018

  • eBook ISBN: 978-3-030-00807-9Published: 14 September 2018

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 180

  • Number of Illustrations: 74 b/w illustrations

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

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