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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Anne L. Martel

    1. University of Toronto, Toronto, Canada

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  2. Purang Abolmaesumi

    1. The University of British Columbia, Vancouver, Canada

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  3. Danail Stoyanov

    1. University College London, London, UK

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  4. Diana Mateus

    1. École Centrale de Nantes, Nantes, France

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  5. Maria A. Zuluaga

    1. EURECOM, Biot, France

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  6. S. Kevin Zhou

    1. Chinese Academy of Sciences, Beijing, China

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  7. Daniel Racoceanu

    1. Sorbonne University, Paris, France

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  8. Leo Joskowicz

    1. The Hebrew University of Jerusalem, Jerusalem, Israel

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12267)

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

Included in the following conference series:

Conference proceedings info: MICCAI 2020.

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About this book

The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.

The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:

Part I: machine learning methodologies

Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks

Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis

Part IV: segmentation; shape models and landmark detection

Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology

Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging

Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

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Table of contents (78 papers)

  1. Front Matter

    Pages i-xxxvii
    Download chapter PDF

  2. Brain Development and Atlases

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer’s Disease

      • Maryam Ghanbari, Li-Ming Hsu, Zhen Zhou, Amir Ghanbari, Zhanhao Mo, Pew-Thian Yap et al.
      Pages 3-12

    3. A Computational Framework for Dissociating Development-Related from Individually Variable Flexibility in Regional Modularity Assignment in Early Infancy

      • Mayssa Soussia, Xuyun Wen, Zhen Zhou, Bing Jin, Tae-Eui Kam, Li-Ming Hsu et al.
      Pages 13-21

    4. Domain-Invariant Prior Knowledge Guided Attention Networks for Robust Skull Stripping of Developing Macaque Brains

      • Tao Zhong, Yu Zhang, Fenqiang Zhao, Yuchen Pei, Lufan Liao, Zhenyuan Ning et al.
      Pages 22-32

    5. Parkinson’s Disease Detection from fMRI-Derived Brainstem Regional Functional Connectivity Networks

      • Nandinee Fariah Haq, Jiayue Cai, Tianze Yu, Martin J. McKeown, Z. Jane Wang
      Pages 33-43

    6. Persistent Feature Analysis of Multimodal Brain Networks Using Generalized Fused Lasso for EMCI Identification

      • Jin Li, Chenyuan Bian, Dandan Chen, Xianglian Meng, Haoran Luo, Hong Liang et al.
      Pages 44-52

    7. Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN Based Generative Adversarial Network

      • Lu Zhang, Li Wang, Dajiang Zhu
      Pages 53-61

    8. From Connectomic to Task-Evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-State Functional Connectivity

      • Gia H. Ngo, Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
      Pages 62-71

    9. Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprinting

      • Dan Hu, Fan Wang, Han Zhang, Zhengwang Wu, Li Wang, Weili Lin et al.
      Pages 72-82

    10. COVLET: Covariance-Based Wavelet-Like Transform for Statistical Analysis of Brain Characteristics in Children

      • Fan Yang, Amal Isaiah, Won Hwa Kim
      Pages 83-93

    11. Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression

      • Tuo Zhang, Zhibin He, Xi Jiang, Lei Guo, Xiaoping Hu, Tianming Liu et al.
      Pages 94-103

    12. Self-weighted Multi-task Learning for Subjective Cognitive Decline Diagnosis

      • Nina Cheng, Alejandro Frangi, Zhi-Guo Zhang, Denao Deng, Lihua Zhao, Tianfu Wang et al.
      Pages 104-113

    13. Unified Brain Network with Functional and Structural Data

      • Jing Yang, Qi Zhu, Rui Zhang, Jiashuang Huang, Daoqiang Zhang
      Pages 114-123

    14. Integrating Similarity Awareness and Adaptive Calibration in Graph Convolution Network to Predict Disease

      • Xuegang Song, Alejandro Frangi, Xiaohua Xiao, Jiuwen Cao, Tianfu Wang, Baiying Lei
      Pages 124-133

    15. Infant Cognitive Scores Prediction with Multi-stream Attention-Based Temporal Path Signature Features

      • Xin Zhang, Jiale Cheng, Hao Ni, Chenyang Li, Xiangmin Xu, Zhengwang Wu et al.
      Pages 134-144

    16. Masked Multi-Task Network for Case-Level Intracranial Hemorrhage Classification in Brain CT Volumes

      • Dongang Wang, Chenyu Wang, Lynette Masters, Michael Barnett
      Pages 145-154

    17. Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates

      • Mustafa Burak Gurbuz, Islem Rekik
      Pages 155-165

    18. Supervised Multi-topology Network Cross-Diffusion for Population-Driven Brain Network Atlas Estimation

      • Islem Mhiri, Mohamed Ali Mahjoub, Islem Rekik
      Pages 166-176

    19. Partial Volume Segmentation of Brain MRI Scans of Any Resolution and Contrast

      • Benjamin Billot, Eleanor Robinson, Adrian V. Dalca, Juan Eugenio Iglesias
      Pages 177-187
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Other volumes

  1. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  2. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  3. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  4. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  5. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  6. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  7. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

  8. Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data

Editors and Affiliations

  • University of Toronto, Toronto, Canada

    Anne L. Martel

  • The University of British Columbia, Vancouver, Canada

    Purang Abolmaesumi

  • University College London, London, UK

    Danail Stoyanov

  • École Centrale de Nantes, Nantes, France

    Diana Mateus

  • EURECOM, Biot, France

    Maria A. Zuluaga

  • Chinese Academy of Sciences, Beijing, China

    S. Kevin Zhou

  • Sorbonne University, Paris, France

    Daniel Racoceanu

  • The Hebrew University of Jerusalem, Jerusalem, Israel

    Leo Joskowicz

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