Simulation and Synthesis in Medical Imaging

1. Front Matter

   Pages i-x

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2. Method-Oriented Papers

   1. Front Matter

      Pages 1-1

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   2. Detail Matters: High-Frequency Content for Realistic Synthetic MRI Generation

      • Filip Rusak, Rodrigo Santa Cruz, Elliot Smith, Jurgen Fripp, Clinton Fookes, Pierrick Bourgeat et al.

      Pages 3-13

   3. Joint Image and Label Self-super-Resolution

      • Samuel W. Remedios, Shuo Han, Blake E. Dewey, Dzung L. Pham, Jerry L. Prince, Aaron Carass

      Pages 14-23

   4. Super-Resolution by Latent Space Exploration: Training with Poorly-Aligned Clinical and Micro CT Image Dataset

      • Tong Zheng, Hirohisa Oda, Yuichiro Hayashi, Shota Nakamura, Masahiro Oda, Kensaku Mori

      Pages 24-33

   5. A Glimpse into the Future: Disease Progression Simulation for Breast Cancer in Mammograms

      • Ibrahim Jubran, Moshiko Raboh, Shaked Perek, David Gruen, Efrat Hexter

      Pages 34-43

   6. Synth-by-Reg (SbR): Contrastive Learning for Synthesis-Based Registration of Paired Images

      • Adrià Casamitjana, Matteo Mancini, Juan Eugenio Iglesias

      Pages 44-54

   7. Learning-Based Template Synthesis for Groupwise Image Registration

      • Ziyi He, Albert C. S. Chung

      Pages 55-66

   8. The Role of MRI Physics in Brain Segmentation CNNs: Achieving Acquisition Invariance and Instructive Uncertainties

      • Pedro Borges, Richard Shaw, Thomas Varsavsky, Kerstin Klaser, David Thomas, Ivana Drobnjak et al.

      Pages 67-76

   9. Transfer Learning in Optical Microscopy

      • Martin Kozlovský, David Wiesner, David Svoboda

      Pages 77-86

   10. X-ray Synthesis Based on Triangular Mesh Models Using GPU-Accelerated Ray Tracing for Multi-modal Breast Image Registration

       • J. Maul, S. Said, N. Ruiter, T. Hopp

       Pages 87-96

3. Application-Oriented Papers

   1. Front Matter

      Pages 97-97

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   2. Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks

      • Michael Gadermayr, Maximilian Tschuchnig, Lea Maria Stangassinger, Christina Kreutzer, Sebastien Couillard-Despres, Gertie Janneke Oostingh et al.

      Pages 99-109

   3. SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image

      • Wanyue Li, Yi He, Wen Kong, Jing Wang, Guohua Deng, Yiwei Chen et al.

      Pages 110-120

   4. Cerebral Blood Volume Prediction Based on Multi-modality Magnetic Resonance Imaging

      • Yongsheng Pan, Jingyu Huang, Bao Wang, Peng Zhao, Yingchao Liu, Yong Xia

      Pages 121-130

   5. Cine-MRI Simulation to Evaluate Tumor Tracking

      • José D. Tascón-Vidarte, Isak Wahlstedt, Julien Jomier, Kenny Erleben, Ivan R. Vogelius, Sune Darkner

      Pages 131-141

   6. GAN-Based Synthetic FDG PET Images from T1 Brain MRI Can Serve to Improve Performance of Deep Unsupervised Anomaly Detection Models

      • Daria Zotova, Julien Jung, Carole Lartizien

      Pages 142-152

4. Correction to: Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks

   • Michael Gadermayr, Maximilian Tschuchnig, Lea Maria Stangassinger, Christina Kreutzer, Sebastien Couillard-Despres, Gertie Janneke Oostingh et al.

   Pages C1-C1

5. Back Matter

   Pages 153-154

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This book constitutes the refereed proceedings of the 6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.*

The 14 full papers presented were carefully reviewed and selected from 18 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/ microscopy image synthesis, and several applications of image synthesis and simulation for data augmentation, image enhancement, or segmentation.

*The workshop was held virtually.

• artificial intelligence
• bioinformatics
• color image processing
• color images
• computer vision
• digital image
• image analysis
• image coding
• image enhancement
• image matching
• image processing
• image quality
• image reconstruction
• image segmentation
• imaging systems
• machine learning
• medical images
• pattern recognition
• reference image

• Masaryk University, Brno, Czech Republic

  David Svoboda

• CNRS – Paris Brain Institute, Paris, France

  Ninon Burgos

• University of Twente, Enschede, The Netherlands

  Jelmer M. Wolterink

• NVIDIA, Santa Clara, USA

  Can Zhao