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Computational Methods and Clinical Applications for Spine Imaging

6th International Workshop and Challenge, CSI 2019, Shenzhen, China, October 17, 2019, Proceedings

  • Conference proceedings
  • © 2020

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Editors:
  1. Yunliang Cai

    1. Worcester Polytechnic Institute, Worcester, USA

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  2. Liansheng Wang

    1. Xiamen University, Xiamen, China

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  3. Michel Audette

    1. Old Dominion University, Norfolk, USA

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  4. Guoyan Zheng

    1. Shanghai Jiao Tong University, Shanghai, China

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  5. Shuo Li

    1. Western University, London, Canada

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11963)

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

Included in the following conference series:

Conference proceedings info: CSI 2019.

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About this book

This book constitutes the proceedings of the 7th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019, which was held in conjunction with MICCAI on October 17, 2019, in Shenzhen, China. All submissions were accepted for publication; the book contains 5 peer-reviewed regular papers, covering topics of vertrebra detection, spine segmentation and image-based diagnosis, and 9 challenge papers, investigating (semi-)automatic spinal curvature estimation algorithms and providing a standard evaluation framework with a set of x-ray images. 

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Table of contents (14 papers)

  1. Front Matter

    Pages i-xii
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  2. Regular Papers

    1. Front Matter

      Pages 1-1
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    2. Detection of Vertebral Fractures in CT Using 3D Convolutional Neural Networks

      • Joeri Nicolaes, Steven Raeymaeckers, David Robben, Guido Wilms, Dirk Vandermeulen, Cesar Libanati et al.
      Pages 3-14

    3. Metastatic Vertebrae Segmentation for Use in a Clinical Pipeline

      • Geoff Klein, Anne Martel, Arjun Sahgal, Cari Whyne, Michael Hardisty
      Pages 15-28

    4. Conditioned Variational Auto-encoder for Detecting Osteoporotic Vertebral Fractures

      • Malek Husseini, Anjany Sekuboyina, Amirhossein Bayat, Bjoern H. Menze, Maximilian Loeffler, Jan S. Kirschke
      Pages 29-38

    5. Vertebral Labelling in Radiographs: Learning a Coordinate Corrector to Enforce Spinal Shape

      • Amirhossein Bayat, Anjany Sekuboyina, Felix Hofmann, Malek El Husseini, Jan S. Kirschke, Bjoern H. Menze
      Pages 39-46

    6. Semi-supervised Semantic Segmentation of Multiple Lumbosacral Structures on CT

      • Huaqing Liu, Haoping Xiao, Lishu Luo, Chaobo Feng, Bangde Yin, Dongdong Wang et al.
      Pages 47-59

  3. AASCE Challenge

    1. Front Matter

      Pages 61-61
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    2. Accurate Automated Keypoint Detections for Spinal Curvature Estimation

      • Kailin Chen, Cheng Peng, Yi Li, Dalong Cheng, Si Wei
      Pages 63-68

    3. Seg4Reg Networks for Automated Spinal Curvature Estimation

      • Yi Lin, Hong-Yu Zhou, Kai Ma, Xin Yang, Yefeng Zheng
      Pages 69-74

    4. Automatic Spine Curvature Estimation by a Top-Down Approach

      • Shixuan Zhao, Bo Wang, Kaifu Yang, Yongjie Li
      Pages 75-80

    5. Automatic Cobb Angle Detection Using Vertebra Detector and Vertebra Corners Regression

      • Bidur Khanal, Lavsen Dahal, Prashant Adhikari, Bishesh Khanal
      Pages 81-87

    6. Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks

      • Florian Dubost, Benjamin Collery, Antonin Renaudier, Axel Roc, Nicolas Posocco, Wiro Niessen et al.
      Pages 88-94

    7. Automated Spinal Curvature Assessment from X-Ray Images Using Landmarks Estimation Network via Rotation Proposals

      • Rong Tao, Shangliang Xu, Haiping Wu, Cheng Zhang, Chuanfeng Lv
      Pages 95-100

    8. A Coarse-to-Fine Deep Heatmap Regression Method for Adolescent Idiopathic Scoliosis Assessment

      • Zhusi Zhong, Jie Li, Zhenxi Zhang, Zhicheng Jiao, Xinbo Gao
      Pages 101-106

    9. Spinal Curve Guide Network (SCG-Net) for Accurate Automated Spinal Curvature Estimation

      • Shuxin Wang, Shaohui Huang, Liansheng Wang
      Pages 107-112

    10. A Multi-task Learning Method for Direct Estimation of Spinal Curvature

      • Jiacheng Wang, Liansheng Wang, Changhua Liu
      Pages 113-118

  4. Back Matter

    Pages 119-120
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Other volumes

  1. Computational Methods and Clinical Applications for Spine Imaging

Editors and Affiliations

  • Worcester Polytechnic Institute, Worcester, USA

    Yunliang Cai

  • Xiamen University, Xiamen, China

    Liansheng Wang

  • Old Dominion University, Norfolk, USA

    Michel Audette

  • Shanghai Jiao Tong University, Shanghai, China

    Guoyan Zheng

  • Western University, London, Canada

    Shuo Li

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