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Patch-Based Techniques in Medical Imaging

Third International Workshop, Patch-MI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings

  • Conference proceedings
  • © 2017

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Editors:
  1. Guorong Wu

    1. University of North Carolina at Chapel Hill, Chapel Hill, USA

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  2. Brent C. Munsell

    1. College of Charleston, Charleston, USA

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  3. Yiqiang Zhan

    1. Siemens Medical Solutions, Malvern, USA

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  4. Wenjia Bai

    1. Imperical College London, London, United Kingdom

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  5. Gerard Sanroma

    1. Pompeu Fabra University, Barcelona, Spain

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  6. Pierrick Coupé

    1. Bordeaux University, Talence Cedex, France

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10530)

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

Included in the following conference series:

Conference proceedings info: Patch-MI 2017.

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About this book

This book constitutes the refereed proceedings of the Third International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2017, which was held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017.

The 18 regular papers presented in this volume were carefully reviewed and selected from 26 submissions. The papers are organized in topical sections on multi-atlas segmentation; segmentation; Alzheimer’s disease; reconstruction, denoising, super-resolution; tumor, lesion; and classification, retrival.

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Table of contents (18 papers)

  1. Front Matter

    Pages I-XI
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  2. Multi-atlas Segmentation

    1. Front Matter

      Pages 1-1
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    2. 4D Multi-atlas Label Fusion Using Longitudinal Images

      • Yuankai Huo, Susan M. Resnick, Bennett A. Landman
      Pages 3-11

    3. Brain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks

      • Longwei Fang, Lichi Zhang, Dong Nie, Xiaohuan Cao, Khosro Bahrami, Huiguang He et al.
      Pages 12-19

    4. Whole Brain Parcellation with Pathology: Validation on Ventriculomegaly Patients

      • Aaron Carass, Muhan Shao, Xiang Li, Blake E. Dewey, Ari M. Blitz, Snehashis Roy et al.
      Pages 20-28

    5. Hippocampus Subfield Segmentation Using a Patch-Based Boosted Ensemble of Autocontext Neural Networks

      • José V. Manjón, Pierrick Coupe
      Pages 29-36

    6. On the Role of Patch Spaces in Patch-Based Label Fusion

      • Oualid M. Benkarim, Gemma Piella, Miguel Angel González Ballester, Gerard Sanroma
      Pages 37-44

  3. Segmentation

    1. Front Matter

      Pages 45-45
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    2. Learning a Sparse Database for Patch-Based Medical Image Segmentation

      • Moti Freiman, Hannes Nickisch, Holger Schmitt, Pal Maurovich-Horvat, Patrick Donnelly, Mani Vembar et al.
      Pages 47-54

    3. Accurate and High Throughput Cell Segmentation Method for Mouse Brain Nuclei Using Cascaded Convolutional Neural Network

      • Qian Wang, Shaoyu Wang, Xiaofeng Zhu, Tianyi Liu, Zachary Humphrey, Vladimir Ghukasyan et al.
      Pages 55-62

  4. Alzheimer’s Disease

    1. Front Matter

      Pages 63-63
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    2. Learning-Based Estimation of Functional Correlation Tensors in White Matter for Early Diagnosis of Mild Cognitive Impairment

      • Lichi Zhang, Han Zhang, Xiaobo Chen, Qian Wang, Pew-Thian Yap, Dinggang Shen
      Pages 65-73

    3. Early Prediction of Alzheimer’s Disease with Non-local Patch-Based Longitudinal Descriptors

      • Gerard Sanroma, Víctor Andrea, Oualid M. Benkarim, José V. Manjón, Pierrick Coupé, Oscar Camara et al.
      Pages 74-81

    4. Adaptive Fusion of Texture-Based Grading: Application to Alzheimer’s Disease Detection

      • Kilian Hett, Vinh-Thong Ta, José V. Manjón, Pierrick Coupé, the Alzheimer’s Disease Neuroimaging Initiative
      Pages 82-89

  5. Reconstruction, Denoising, Super-Resolution

    1. Front Matter

      Pages 91-91
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    2. Micro-CT Guided 3D Reconstruction of Histological Images

      • Kai Nagara, Holger R. Roth, Shota Nakamura, Hirohisa Oda, Takayasu Moriya, Masahiro Oda et al.
      Pages 93-101

    3. A Neural Regression Framework for Low-Dose Coronary CT Angiography (CCTA) Denoising

      • Michael Green, Edith M. Marom, Nahum Kiryati, Eli Konen, Arnaldo Mayer
      Pages 102-110

    4. A Dictionary Learning-Based Fast Imaging Method for Ultrasound Elastography

      • Manyou Ma, Robert Rohling, Lutz Lampe
      Pages 111-118

  6. Tumor, Lesion

    1. Front Matter

      Pages 119-119
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    2. Breast Tumor Detection in Ultrasound Images Using Deep Learning

      • Zhantao Cao, Lixin Duan, Guowu Yang, Ting Yue, Qin Chen, Huazhu Fu et al.
      Pages 121-128
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Other volumes

  1. Patch-Based Techniques in Medical Imaging

Editors and Affiliations

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

    Guorong Wu

  • College of Charleston, Charleston, USA

    Brent C. Munsell

  • Siemens Medical Solutions, Malvern, USA

    Yiqiang Zhan

  • Imperical College London, London, United Kingdom

    Wenjia Bai

  • Pompeu Fabra University, Barcelona, Spain

    Gerard Sanroma

  • Bordeaux University, Talence Cedex, France

    Pierrick Coupé

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