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Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology

Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Seyed Mostafa Kia

    1. Donders Institute, Nijmegen, The Netherlands

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  2. Hassan Mohy-ud-Din

    1. Lahore University of Management Sciences, Lahore, Pakistan

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  3. Ahmed Abdulkadir

    1. University of Pennsylvania, Philadelphia, USA

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  4. Cher Bass

    1. King’s College London, London, UK

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  5. Mohamad Habes

    1. The University of Texas Health Science Center at San Antonio, San Antonio, USA

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  6. Jane Maryam Rondina

    1. University College London, London, UK

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  7. Chantal Tax

    1. CUBRIC, Cardiff, UK

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  8. Hongzhi Wang

    1. IBM Almaden Research Center, San Jose, USA

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  9. Thomas Wolfers

    1. University of Oslo, Oslo, Norway

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  10. Saima Rathore

    1. Eli Lilly Pharmaceutical Company, Philadelphia, USA

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  11. Madhura Ingalhalikar

    1. Symbiosis Institute of Technology, Pune, India

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12449)

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

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Table of contents (29 papers)

  1. Front Matter

    Pages i-xviii
    Download chapter PDF

  2. MLCN 2020

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Surface Agnostic Metrics for Cortical Volume Segmentation and Regression

      • Samuel Budd, Prachi Patkee, Ana Baburamani, Mary Rutherford, Emma C. Robinson, Bernhard Kainz
      Pages 3-12

    3. Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy

      • Gabriele Amorosino, Denis Peruzzo, Pietro Astolfi, Daniela Redaelli, Paolo Avesani, Filippo Arrigoni et al.
      Pages 13-22

    4. Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows

      • Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner et al.
      Pages 23-33

    5. A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity

      • Naresh Nandakumar, Niharika Shimona D’Souza, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair et al.
      Pages 34-44

    6. Deep Learning for Non-invasive Cortical Potential Imaging

      • Alexandra Razorenova, Nikolay Yavich, Mikhail Malovichko, Maxim Fedorov, Nikolay Koshev, Dmitry V. Dylov
      Pages 45-55

    7. An Anatomically-Informed 3D CNN for Brain Aneurysm Classification with Weak Labels

      • Tommaso Di Noto, Guillaume Marie, Sébastien Tourbier, Yasser Alemán-Gómez, Guillaume Saliou, Meritxell Bach Cuadra et al.
      Pages 56-66

    8. Ischemic Stroke Segmentation from CT Perfusion Scans Using Cluster-Representation Learning

      • Jianyuan Zhang, Feng Shi, Lei Chen, Zhong Xue, Lichi Zhang, Dahong Qian
      Pages 67-76

    9. SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

      • Umar Asif, Subhrajit Roy, Jianbin Tang, Stefan Harrer
      Pages 77-87

    10. Decoding Task States by Spotting Salient Patterns at Time Points and Brain Regions

      • Yi Hao Chan, Sukrit Gupta, L. L. Chamara Kasun, Jagath C. Rajapakse
      Pages 88-97

    11. Patch-Based Brain Age Estimation from MR Images

      • Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Arinbjörn Kolbeinsson, Alexander Hammers, Daniel Rueckert
      Pages 98-107

    12. Large-Scale Unbiased Neuroimage Indexing via 3D GPU-SIFT Filtering and Keypoint Masking

      • Étienne Pepin, Jean-Baptiste Carluer, Laurent Chauvin, Matthew Toews, Rola Harmouche
      Pages 108-118

    13. A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

      • Stefano Cerri, Andrew Hoopes, Douglas N. Greve, Mark Mühlau, Koen Van Leemput
      Pages 119-128

    14. Towards MRI Progression Features for Glioblastoma Patients: From Automated Volumetry and Classical Radiomics to Deep Feature Learning

      • Yannick Suter, Urspeter Knecht, Roland Wiest, Ekkehard Hewer, Philippe Schucht, Mauricio Reyes
      Pages 129-138

    15. Generalizing MRI Subcortical Segmentation to Neurodegeneration

      • Hao Li, Huahong Zhang, Dewei Hu, Hans Johnson, Jeffrey D. Long, Jane S. Paulsen et al.
      Pages 139-147

    16. Multiple Sclerosis Lesion Segmentation Using Longitudinal Normalization and Convolutional Recurrent Neural Networks

      • Sergio Tascon-Morales, Stefan Hoffmann, Martin Treiber, Daniel Mensing, Arnau Oliver, Matthias Guenther et al.
      Pages 148-158

    17. Deep Voxel-Guided Morphometry (VGM): Learning Regional Brain Changes in Serial MRI

      • Alena-Kathrin Schnurr, Philipp Eisele, Christina Rossmanith, Stefan Hoffmann, Johannes Gregori, Andreas Dabringhaus et al.
      Pages 159-168

    18. A Deep Transfer Learning Framework for 3D Brain Imaging Based on Optimal Mass Transport

      • Ling-Li Zeng, Christopher R. K. Ching, Zvart Abaryan, Sophia I. Thomopoulos, Kai Gao, Alyssa H. Zhu et al.
      Pages 169-176

    19. Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images

      • Guy Leroy, Daniel Rueckert, Amir Alansary
      Pages 177-186
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  1. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology

Keywords

About this book

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.*

For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging.

For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience.

*The workshops were held virtually due to the COVID-19 pandemic.

Editors and Affiliations

  • Donders Institute, Nijmegen, The Netherlands

    Seyed Mostafa Kia

  • Lahore University of Management Sciences, Lahore, Pakistan

    Hassan Mohy-ud-Din

  • University of Pennsylvania, Philadelphia, USA

    Ahmed Abdulkadir

  • King’s College London, London, UK

    Cher Bass

  • The University of Texas Health Science Center at San Antonio, San Antonio, USA

    Mohamad Habes

  • University College London, London, UK

    Jane Maryam Rondina

  • CUBRIC, Cardiff, UK

    Chantal Tax

  • IBM Almaden Research Center, San Jose, USA

    Hongzhi Wang

  • University of Oslo, Oslo, Norway

    Thomas Wolfers

  • Eli Lilly Pharmaceutical Company, Philadelphia, USA

    Saima Rathore

  • Symbiosis Institute of Technology, Pune, India

    Madhura Ingalhalikar

Bibliographic Information

  • Book Title: Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology

  • Book Subtitle: Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

  • Editors: Seyed Mostafa Kia, Hassan Mohy-ud-Din, Ahmed Abdulkadir, Cher Bass, Mohamad Habes, Jane Maryam Rondina, Chantal Tax, Hongzhi Wang, Thomas Wolfers, Saima Rathore, … Madhura Ingalhalikar

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-66843-3

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Softcover ISBN: 978-3-030-66842-6Published: 31 December 2020

  • eBook ISBN: 978-3-030-66843-3Published: 30 December 2020

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVIII, 305

  • Number of Illustrations: 8 b/w illustrations

  • Topics: Image Processing and Computer Vision

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