Graph Learning in Medical Imaging

1. Front Matter

   Pages i-ix

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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

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.

• 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

• Nanjing University of Aeronautics and Astronautics, Nanjing, China

  Daoqiang Zhang

• University of Sydney, Sydney, Australia

  Luping Zhou

• Anhui Normal University, Wuhu, China

  Biao Jie

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

  Mingxia Liu

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