Simulation and synthesis in medical imaging : third international workshop, SASHIMI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018 : proceedings

This book constitutes the refereed proceedings of the Third International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 14 full papers presented were carefully reviewed and selected from numerous submissions. This workshop continues to provide a state-of-the-art and integrative perspective on simulation and synthesis in medical imaging for the purpose of invigorating research and stimulating new ideas on how to build theoretical links, practical synergies, and best practices between these two research directions. Front Matter ....Pages I-X Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks (Hoo-Chang Shin, Neil A. Tenenholtz, Jameson K. Rogers, Christopher G. Schwarz, Matthew L. Senjem, Jeffrey L. Gunter et al.)....Pages 1-11 Data Augmentation Using Synthetic Lesions Improves Machine Learning Detection of Microbleeds from MRI (Saba Momeni, Amir Fazllolahi, Pierrick Bourgeat, Parnesh Raniga, Paul Yates, Nawaf Yassi et al.)....Pages 12-19 Deep Harmonization of Inconsistent MR Data for Consistent Volume Segmentation (Blake E. Dewey, Can Zhao, Aaron Carass, Jiwon Oh, Peter A. Calabresi, Peter C. M. van Zijl et al.)....Pages 20-30 Cross-Modality Image Synthesis from Unpaired Data Using CycleGAN (Yuta Hiasa, Yoshito Otake, Masaki Takao, Takumi Matsuoka, Kazuma Takashima, Aaron Carass et al.)....Pages 31-41 A Machine Learning Approach to Diffusion MRI Partial Volume Estimation (Emmanuel Vallee, Wenchuan Wu, Francesca Galassi, Saad Jbabdi, Stephen Smith)....Pages 42-51 Unsupervised Learning for Cross-Domain Medical Image Synthesis Using Deformation Invariant Cycle Consistency Networks (Chengjia Wang, Gillian Macnaught, Giorgos Papanastasiou, Tom MacGillivray, David Newby)....Pages 52-60 Deep Boosted Regression for MR to CT Synthesis (Kerstin Kläser, Pawel Markiewicz, Marta Ranzini, Wenqi Li, Marc Modat, Brian F. Hutton et al.)....Pages 61-70 Model-Based Generation of Synthetic 3D Time-Lapse Sequences of Multiple Mutually Interacting Motile Cells with Filopodia (Igor Peterlík, David Svoboda, Vladimír Ulman, Dmitry V. Sorokin, Martin Maška)....Pages 71-79 MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net for Multi-modal Alzheimer’s Classification (Apoorva Sikka, Skand Vishwanath Peri, Deepti R. Bathula)....Pages 80-89 Tubular Network Formation Process Using 3D Cellular Potts Model (David Svoboda, Tereza Nečasová, Lenka Tesařová, Pavel Šimara)....Pages 90-99 Deep Learning Based Coronary Artery Motion Artifact Compensation Using Style-Transfer Synthesis in CT Images (Sunghee Jung, Soochahn Lee, Byunghwan Jeon, Yeonggul Jang, Hyuk-Jae Chang)....Pages 100-110 Lung Nodule Synthesis Using CNN-Based Latent Data Representation (Dario Augusto Borges Oliveira, Matheus Palhares Viana)....Pages 111-118 RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours (Raghav Mehta, Tal Arbel)....Pages 119-129 Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-linear and Deep Learning Models (Nathan Olliverre, Guang Yang, Gregory Slabaugh, Constantino Carlos Reyes-Aldasoro, Eduardo Alonso)....Pages 130-138 Back Matter ....Pages 139-140