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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II

Conference proceedings info: MICCAI 2019.

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Table of contents (96 papers)

  1. Front Matter

    Pages i-xxxvi
  2. Image Segmentation

    1. Front Matter

      Pages 1-1
    2. Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation

      • Dong Yang, Holger Roth, Ziyue Xu, Fausto Milletari, Ling Zhang, Daguang Xu
      Pages 3-11
    3. Comparative Evaluation of Hand-Engineered and Deep-Learned Features for Neonatal Hip Bone Segmentation in Ultrasound

      • Houssam El-Hariri, Kishore Mulpuri, Antony Hodgson, Rafeef Garbi
      Pages 12-20
    4. One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation

      • Mathias Perslev, Erik Bjørnager Dam, Akshay Pai, Christian Igel
      Pages 30-38
    5. ‘Project & Excite’ Modules for Segmentation of Volumetric Medical Scans

      • Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua, Nassir Navab, Christian Wachinger
      Pages 39-47
    6. Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation

      • Cheng Li, Hui Sun, Zaiyi Liu, Meiyun Wang, Hairong Zheng, Shanshan Wang
      Pages 57-65
    7. Extreme Points Derived Confidence Map as a Cue for Class-Agnostic Interactive Segmentation Using Deep Neural Network

      • Shadab Khan, Ahmed H. Shahin, Javier Villafruela, Jianbing Shen, Ling Shao
      Pages 66-73
    8. Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation

      • Reuben Dorent, Samuel Joutard, Marc Modat, Sébastien Ourselin, Tom Vercauteren
      Pages 74-82
    9. Instance Segmentation from Volumetric Biomedical Images Without Voxel-Wise Labeling

      • Meng Dong, Dong Liu, Zhiwei Xiong, Xuejin Chen, Yueyi Zhang, Zheng-Jun Zha et al.
      Pages 83-91
    10. Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice

      • Jeroen Bertels, Tom Eelbode, Maxim Berman, Dirk Vandermeulen, Frederik Maes, Raf Bisschops et al.
      Pages 92-100
    11. Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation

      • Xianxu Hou, Jingxin Liu, Bolei Xu, Bozhi Liu, Xin Chen, Mohammad Ilyas et al.
      Pages 101-109
    12. HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images

      • Haozhe Jia, Yang Song, Heng Huang, Weidong Cai, Yong Xia
      Pages 110-118
    13. PHiSeg: Capturing Uncertainty in Medical Image Segmentation

      • Christian F. Baumgartner, Kerem C. Tezcan, Krishna Chaitanya, Andreas M. Hötker, Urs J. Muehlematter, Khoschy Schawkat et al.
      Pages 119-127
    14. Supervised Uncertainty Quantification for Segmentation with Multiple Annotations

      • Shi Hu, Daniel Worrall, Stefan Knegt, Bas Veeling, Henkjan Huisman, Max Welling
      Pages 137-145
    15. Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

      • Yuyin Zhou, Yingwei Li, Zhishuai Zhang, Yan Wang, Angtian Wang, Elliot K. Fishman et al.
      Pages 155-163

About this book

The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019.

The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections:

Part I: optical imaging; endoscopy; microscopy.

Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression.

Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging.

Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis.

Part V: computer assisted interventions; MIC meets CAI.

Part VI: computed tomography; X-ray imaging.

Editors and Affiliations

  • University of North Carolina at Chapel Hill, Chapel Hill, USA

    Dinggang Shen, Pew-Thian Yap

  • University of Georgia, Athens, USA

    Tianming Liu

  • Western University, London, Canada

    Terry M. Peters, Ali Khan

  • Yale University, New Haven, USA

    Lawrence H. Staib

  • University of Strasbourg, Illkirch, France

    Caroline Essert

  • United Imaging Intelligence, Shanghai, China

    Sean Zhou

Bibliographic Information

  • Book Title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  • Book Subtitle: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II

  • Editors: Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-32245-8

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Softcover ISBN: 978-3-030-32244-1Published: 18 October 2019

  • eBook ISBN: 978-3-030-32245-8Published: 10 October 2019

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXXVI, 874

  • Number of Illustrations: 35 b/w illustrations, 312 illustrations in colour

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

Buy it now

Buying options

eBook USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access