Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

1. Front Matter

   Pages I-XXV

   PDF

2. Feature Selection Based on Iterative Canonical Correlation Analysis for Automatic Diagnosis of Parkinson’s Disease

   • Luyan Liu, Qian Wang, Ehsan Adeli, Lichi Zhang, Han Zhang, Dinggang Shen

   Pages 1-8

3. Identifying Relationships in Functional and Structural Connectome Data Using a Hypergraph Learning Method

   • Brent C. Munsell, Guorong Wu, Yue Gao, Nicholas Desisto, Martin Styner

   Pages 9-17

4. Ensemble Hierarchical High-Order Functional Connectivity Networks for MCI Classification

   • Xiaobo Chen, Han Zhang, Dinggang Shen

   Pages 18-25

5. Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networks

   • Luyan Liu, Han Zhang, Islem Rekik, Xiaobo Chen, Qian Wang, Dinggang Shen

   Pages 26-34

6. Mammographic Mass Segmentation with Online Learned Shape and Appearance Priors

   • Menglin Jiang, Shaoting Zhang, Yuanjie Zheng, Dimitris N. Metaxas

   Pages 35-43

7. Differential Dementia Diagnosis on Incomplete Data with Latent Trees

   • Christian Ledig, Sebastian Kaltwang, Antti Tolonen, Juha Koikkalainen, Philip Scheltens, Frederik Barkhof et al.

   Pages 44-52

8. Bridging Computational Features Toward Multiple Semantic Features with Multi-task Regression: A Study of CT Pulmonary Nodules

   • Sihong Chen, Dong Ni, Jing Qin, Baiying Lei, Tianfu Wang, Jie-Zhi Cheng

   Pages 53-60

9. Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PET Images

   • Chunfeng Lian, Su Ruan, Thierry Denœux, Hua Li, Pierre Vera

   Pages 61-69

10. Structured Sparse Kernel Learning for Imaging Genetics Based Alzheimer’s Disease Diagnosis

    • Jailin Peng, Le An, Xiaofeng Zhu, Yan Jin, Dinggang Shen

    Pages 70-78

11. Semi-supervised Hierarchical Multimodal Feature and Sample Selection for Alzheimer’s Disease Diagnosis

    • Le An, Ehsan Adeli, Mingxia Liu, Jun Zhang, Dinggang Shen

    Pages 79-87

12. Stability-Weighted Matrix Completion of Incomplete Multi-modal Data for Disease Diagnosis

    • Kim-Han Thung, Ehsan Adeli, Pew-Thian Yap, Dinggang Shen

    Pages 88-96

13. Employing Visual Analytics to Aid the Design of White Matter Hyperintensity Classifiers

    • Renata Georgia Raidou, Hugo J. Kuijf, Neda Sepasian, Nicola Pezzotti, Willem H. Bouvy, Marcel Breeuwer et al.

    Pages 97-105

14. The Automated Learning of Deep Features for Breast Mass Classification from Mammograms

    • Neeraj Dhungel, Gustavo Carneiro, Andrew P. Bradley

    Pages 106-114

15. Multimodal Deep Learning for Cervical Dysplasia Diagnosis

    • Tao Xu, Han Zhang, Xiaolei Huang, Shaoting Zhang, Dimitris N. Metaxas

    Pages 115-123

16. Learning from Experts: Developing Transferable Deep Features for Patient-Level Lung Cancer Prediction

    • Wei Shen, Mu Zhou, Feng Yang, Di Dong, Caiyun Yang, Yali Zang et al.

    Pages 124-131

17. DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field

    • Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Jiang Liu

    Pages 132-139

18. Deep Retinal Image Understanding

    • Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbeláez, Luc Van Gool

    Pages 140-148

19. 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

    • Qi Dou, Hao Chen, Yueming Jin, Lequan Yu, Jing Qin, Pheng-Ann Heng

    Pages 149-157

20. Deep Neural Networks for Fast Segmentation of 3D Medical Images

    • Karl Fritscher, Patrik Raudaschl, Paolo Zaffino, Maria Francesca Spadea, Gregory C. Sharp, Rainer Schubert

    Pages 158-165

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation;  shape modeling; cardiac and vascular image analysis; image reconstruction; and MR imageanalysis.

• biomechanical simulation
• brain network analysis
• computer aided diagnosis
• robot intervention
• surgical simulation
• augmented reality
• classification
• clustering
• computational anatomy
• computational geometry
• deep neural networks
• image analysis
• intervention planning
• medical imaging
• navigation
• normalization
• statistical atlases
• support vector machines
• visualization
• x-ray imaging

• University College London , London, United Kingdom

  Sebastien Ourselin

• The Hebrew University of Jerusalem , Jerusalem, Israel

  Leo Joskowicz

• Harvard Medical School , Boston, USA

  Mert R. Sabuncu

• Istanbul Technical University , Istanbul, Turkey

  Gozde Unal

• Harvard Medical School and Brigham and Women's Hospital, Boston, USA

  William Wells

Policies and ethics