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Graph Learning in Medical Imaging

First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

  • Daoqiang Zhang
  • Luping Zhou
  • Biao Jie
  • Mingxia Liu
Conference proceedings GLMI 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11849)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11849)

Table of contents

  1. Front Matter
    Pages i-ix
    PDF
  2. Graph Hyperalignment for Multi-subject fMRI Functional Alignment
    Weida Li, Fang Chen, Daoqiang Zhang
    Pages 1-8
  3. Interactive 3D Segmentation Editing and Refinement via Gated Graph Neural Networks
    Xiaosong Wang, Ling Zhang, Holger Roth, Daguang Xu, Ziyue Xu
    Pages 9-17
  4. Adaptive Thresholding of Functional Connectivity Networks for fMRI-Based Brain Disease Analysis
    Zhengdong Wang, Biao Jie, Weixin Bian, Daoqiang Zhang, Dinggang Shen, Mingxia Liu
    Pages 18-26
  5. Graph-Kernel-Based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Networks for Brain Disease Classification
    Zhengdong Wang, Biao Jie, Mi Wang, Chunxiang Feng, Wen Zhou, Dinggang Shen et al.
    Pages 27-35
  6. Linking Convolutional Neural Networks with Graph Convolutional Networks: Application in Pulmonary Artery-Vein Separation
    Zhiwei Zhai, Marius Staring, Xuhui Zhou, Qiuxia Xie, Xiaojuan Xiao, M. Els Bakker et al.
    Pages 36-43
  7. Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction
    Oleh Dzyubachyk, Kirsten Koolstra, Nicola Pezzotti, Boudewijn P. F. Lelieveldt, Andrew Webb, Peter Börnert
    Pages 44-52
  8. Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients
    Lasse Hansen, Doris Dittmer, Mattias P. Heinrich
    Pages 53-61
  9. Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography
    Jelmer M. Wolterink, Tim Leiner, Ivana Išgum
    Pages 62-69
  10. Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI
    Dongren Yao, Mingxia Liu, Mingliang Wang, Chunfeng Lian, Jie Wei, Li Sun et al.
    Pages 70-78
  11. Multi-scale Graph Convolutional Network for Mild Cognitive Impairment Detection
    Shuangzhi Yu, Guanghui Yue, Ahmed Elazab, Xuegang Song, Tianfu Wang, Baiying Lei
    Pages 79-87
  12. DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks
    Feihong Liu, Jun Feng, Geng Chen, Ye Wu, Yoonmi Hong, Pew-Thian Yap et al.
    Pages 88-95
  13. Identification of Functional Connectivity Features in Depression Subtypes Using a Data-Driven Approach
    Xingjuan Li, Samantha Burnham, Jurgen Fripp, Yu Li, Xue Li, Amir Fazlollahi et al.
    Pages 96-103
  14. Movie-Watching fMRI Reveals Inter-subject Synchrony Alteration in Functional Brain Activity in ADHD
    Chao Tang, Ziyi Huang, Senyu Zhou, Qi Wang, Fa Yi, Jingxin Nie
    Pages 104-111
  15. Weakly- and Semi-supervised Graph CNN for Identifying Basal Cell Carcinoma on Pathological Images
    Junyan Wu, Jia-Xing Zhong, Eric Z. Chen, Jingwei Zhang, Jay J. Ye, Limin Yu
    Pages 112-119
  16. Geometric Brain Surface Network for Brain Cortical Parcellation
    Wen Zhang, Yalin Wang
    Pages 120-129
  17. Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images Using 3D Mask R-CNN
    Yankun Lang, Li Wang, Pew-Thian Yap, Chunfeng Lian, Hannah Deng, Kim-Han Thung et al.
    Pages 130-137
  18. Discriminative-Region-Aware Residual Network for Adolescent Brain Structure and Cognitive Development Analysis
    Yongsheng Pan, Mingxia Liu, Li Wang, Yong Xia, Dinggang Shen
    Pages 138-146
  19. Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram
    Shelda Sajeev, Mariusz Bajger, Gobert Lee
    Pages 147-154
  20. OCD Diagnosis via Smoothing Sparse Network and Stacked Sparse Auto-Encoder Learning
    Peng Yang, Lili Jin, Chuangyong Xu, Tianfu Wang, Baiying Lei, Ziwen Peng
    Pages 155-163
  21. A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism
    Guannan Li, Meng-Hsiang Chen, Gang Li, Di Wu, Chunfeng Lian, Quansen Sun et al.
    Pages 164-171
  22. CNS: CycleGAN-Assisted Neonatal Segmentation Model for Cross-Datasets
    Jian Chen, Zhenghan Fang, Deqiang Xiao, Duc Toan Bui, Kim-Han Thung, Xianjun Li et al.
    Pages 172-179
  23. Back Matter
    Pages 181-182
    PDF

About these proceedings

Introduction

This book constitutes the refereed proceedings of the First International Workshop on Graph Learning in Medical Imaging, GLMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019.

The 21 full papers presented were carefully reviewed and selected from 42 submissions. The papers focus on major trends and challenges of graph learning in medical imaging and present original work aimed to identify new cutting-edge techniques and their applications in medical imaging.

Keywords

artificial intelligence computer aided diagnosis computer networks computer systems computer vision deep learning graph theory image processing image reconstruction image segmentation machine learning mathematics medical image analysis medical imaging data analytics molecular imaging network architecture network protocols neural networks pattern recognition signal processing

Editors and affiliations

  1. 1.Nanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.University of SydneySydneyAustralia
  3. 3.Anhui Normal UniversityWuhuChina
  4. 4.University of North Carolina at Chapel HillChapel HillUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-35817-4
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-35816-7
  • Online ISBN 978-3-030-35817-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site