Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

1. Front Matter

   Pages i-xxxvii

   PDF

2. Segmentation

   1. Front Matter

      Pages 1-1

      PDF

   2. Deep Volumetric Universal Lesion Detection Using Light-Weight Pseudo 3D Convolution and Surface Point Regression

      • Jinzheng Cai, Ke Yan, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao, Le Lu et al.

      Pages 3-13

   3. DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision

      • Julia Wolleb, Robin Sandkühler, Philippe C. Cattin

      Pages 14-24

   4. KISEG: A Three-Stage Segmentation Framework for Multi-level Acceleration of Chest CT Scans from COVID-19 Patients

      • Xiaohong Liu, Kai Wang, Ke Wang, Ting Chen, Kang Zhang, Guangyu Wang

      Pages 25-34

   5. CircleNet: Anchor-Free Glomerulus Detection with Circle Representation

      • Haichun Yang, Ruining Deng, Yuzhe Lu, Zheyu Zhu, Ye Chen, Joseph T. Roland et al.

      Pages 35-44

   6. Weakly Supervised One-Stage Vision and Language Disease Detection Using Large Scale Pneumonia and Pneumothorax Studies

      • Leo K. Tam, Xiaosong Wang, Evrim Turkbey, Kevin Lu, Yuhong Wen, Daguang Xu

      Pages 45-55

   7. Diagnostic Assessment of Deep Learning Algorithms for Detection and Segmentation of Lesion in Mammographic Images

      • Thomas Boot, Humayun Irshad

      Pages 56-65

   8. Efficient and Phase-Aware Video Super-Resolution for Cardiac MRI

      • Jhih-Yuan Lin, Yu-Cheng Chang, Winston H. Hsu

      Pages 66-76

   9. ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease

      • Xiaowei Xu, Tianchen Wang, Jian Zhuang, Haiyun Yuan, Meiping Huang, Jianzheng Cen et al.

      Pages 77-87

   10. Deep Generative Model-Based Quality Control for Cardiac MRI Segmentation

       • Shuo Wang, Giacomo Tarroni, Chen Qin, Yuanhan Mo, Chengliang Dai, Chen Chen et al.

       Pages 88-97

   11. DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation

       • Shunjie Dong, Jinlong Zhao, Maojun Zhang, Zhengxue Shi, Jianing Deng, Yiyu Shi et al.

       Pages 98-107

   12. Learning Directional Feature Maps for Cardiac MRI Segmentation

       • Feng Cheng, Cheng Chen, Yukang Wang, Heshui Shi, Yukun Cao, Dandan Tu et al.

       Pages 108-117

   13. Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention

       • Lei Li, Xin Weng, Julia A. Schnabel, Xiahai Zhuang

       Pages 118-127

   14. XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms

       • Sina Amirrajab, Samaneh Abbasi-Sureshjani, Yasmina Al Khalil, Cristian Lorenz, Jürgen Weese, Josien Pluim et al.

       Pages 128-137

   15. TexNet: Texture Loss Based Network for Gastric Antrum Segmentation in Ultrasound

       • Guohao Dong, Yaoxian Zou, Jiaming Jiao, Yuxi Liu, Shuo Liu, Tianzhu Liang et al.

       Pages 138-145

   16. Multi-organ Segmentation via Co-training Weight-Averaged Models from Few-Organ Datasets

       • Rui Huang, Yuanjie Zheng, Zhiqiang Hu, Shaoting Zhang, Hongsheng Li

       Pages 146-155

   17. Suggestive Annotation of Brain Tumour Images with Gradient-Guided Sampling

       • Chengliang Dai, Shuo Wang, Yuanhan Mo, Kaichen Zhou, Elsa Angelini, Yike Guo et al.

       Pages 156-165

   18. Pay More Attention to Discontinuity for Medical Image Segmentation

       • Jiajia Chu, Yajie Chen, Wei Zhou, Heshui Shi, Yukun Cao, Dandan Tu et al.

       Pages 166-175

   19. Learning 3D Features with 2D CNNs via Surface Projection for CT Volume Segmentation

       • Youyi Song, Zhen Yu, Teng Zhou, Jeremy Yuen-Chun Teoh, Baiying Lei, Kup-Sze Choi et al.

       Pages 176-186

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

• artificial intelligence
• bioinformatics
• computer aided diagnosis
• computer vision
• image analysis
• image processing
• image reconstruction
• image segmentation
• imaging systems
• machine learning
• medical image analysis
• medical images
• neural networks
• object recognition
• pattern recognition
• segmentation methods
• signal processing

• 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

Policies and ethics