Simulation and Synthesis in Medical Imaging: 5th International Workshop, SASHIMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings

206 Pages·2020·36.599 MB·English

by  Ninon Burgos, David Svoboda, Jelmer M. Wolterink, Can Zhao

Ninon Burgos
David Svoboda
Jelmer M. Wolterink
Can Zhao (Eds.)
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in Medical Imaging
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5th International Workshop, SASHIMI 2020
Held in Conjunction with MICCAI 2020
Lima, Peru, October 4, 2020
Proceedings
Lecture Notes in Computer Science 12417
Founding Editors
Gerhard Goos
Karlsruhe Institute of Technology, Karlsruhe, Germany
Juris Hartmanis
Cornell University, Ithaca, NY, USA
Editorial Board Members
Elisa Bertino
Purdue University, West Lafayette, IN, USA
Wen Gao
Peking University, Beijing, China
Bernhard Steffen
TU Dortmund University, Dortmund, Germany
Gerhard Woeginger
RWTH Aachen, Aachen, Germany
Moti Yung
Columbia University, New York, NY, USA
More information about this series at http://www.springer.com/series/7412
Ninon Burgos David Svoboda
(cid:129) (cid:129)
Jelmer M. Wolterink Can Zhao (Eds.)
(cid:129)
Simulation and Synthesis
in Medical Imaging
5th International Workshop, SASHIMI 2020
Held in Conjunction with MICCAI 2020
Lima, Peru, October 4, 2020
Proceedings
123
Editors
NinonBurgos DavidSvoboda
CNRS– Paris Brain Institute MasarykUniversity
Paris, France Brno, Czech Republic
Jelmer M.Wolterink CanZhao
University of Twente Johns HopkinsUniversity
Enschede,The Netherlands Bethesda, MD, USA
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Computer Science
ISBN 978-3-030-59519-7 ISBN978-3-030-59520-3 (eBook)
https://doi.org/10.1007/978-3-030-59520-3
LNCSSublibrary:SL6–ImageProcessing,ComputerVision,PatternRecognition,andGraphics
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Preface
The Medical Image Computing and Computer Assisted Intervention (MICCAI)
community needs data with known ground truth to develop, evaluate, and validate
computerized image analytic tools, as well as to facilitate clinical training. Since
synthetic data are ideally suited for this purpose, a full range of models underpinning
image simulation and synthesis, also referred to as image translation, cross-modality
synthesis,imagecompletion,etc.,havebeendevelopedovertheyears:(a)machineand
deeplearningmethodsbasedongenerativemodels;(b)simplifiedmathematicalmodels
to test segmentation, tracking, restoration, and registration algorithms; (c) detailed
mechanisticmodels(top–down),whichincorporatepriorsonthegeometryandphysics
of image acquisition and formation processes; and (d) complex spatio-temporal com-
putational models of anatomical variability, organ physiology, and morphological
changes in tissues or disease progression.
The goal of the Simulation and Synthesis in Medical Imaging (SASHIMI)1 work-
shop is to bring together all those interested in such problems in order to engage in
invigorating research, discuss current approaches, and stimulate new ideas and scien-
tific directions in this field. The objectives are to (a) bring together experts on image
synthesis to raise the state of the art; (b) hear from invited speakers outside of the
MICCAI community, for example in the areas of transfer learning, generative adver-
sarial networks, or variational autoencoders, to cross-fertilize these fields; and
(c) identify challengesandopportunities forfurtherresearch.Wealso wanttoidentify
the suitable approaches to evaluate the plausibility of synthetic data and to collect
benchmark data that could help with the development offuture algorithms.
The 5th SASHIMI workshop was successfully held in conjunction with the 23rd
International Conference on Medical Image Computing and Computer-Assisted
Intervention (MICCAI 2020) as a satellite event on October 4, 2020. Submissions
weresolicitedviaacallforpaperscirculatedbytheMICCAIorganizers,viathesocial
media,aswellasbydirectlyemailingcolleaguesandexpertsinthearea.Eachofthe27
submissionsreceivedunderwentadouble-blindreviewbyatleasttwomembersofthe
Program Committee, consisting of researchers actively contributing in the area.
Compared to the 2019 edition, we saw an increased number of submissions and
diversity of covered topics. At the conclusion of the review process, 19 papers were
accepted. Overall, 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.
We would like to thank everyone who contributed to this 5th workshop: members
oftheOrganizingCommitteefortheirassistance,theauthorsfortheircontributions,the
membersoftheProgramCommitteefortheirpreciousreviewworks,promotionsofthe
1 http://www.sashimi.aramislab.fr.
vi Preface
workshop, and general support. We are particularly grateful to the invited speaker,
Dr.Ming-YuLiu,whokindlysharedhisexpertiseandknowledgewiththecommunity.
WealsothankthemembersoftheSteeringCommitteefortheiradviceandsupport,and
the MICCAI society for the general support.
October 2020 Ninon Burgos
David Svoboda
Jelmer M. Wolterink
Can Zhao
Organization
Program Chairs
Ninon Burgos CNRS, Paris Brain Institute, France
David Svoboda Masaryk University, Czech Republic
Jelmer M. Wolterink University of Twente, The Netherlands
Can Zhao NVIDIA, Johns Hopkins University, USA
Steering Committee
Sotirios A. Tsaftaris The University of Edinburgh, UK
Alejandro F. Frangi The University of Sheffield, UK
Jerry L. Prince Johns Hopkins University, USA
Program Committee
Ninon Burgos CNRS, Paris Brain Institute, France
Aaron Carass Johns Hopkins University, USA
Blake Dewey Johns Hopkins University, USA
Florian Dubost Erasmus MC, The Netherlands
Hamid Fehri University of Oxford, UK
Thomas Joyce ETH Zürich, Switzerland
Martin Maška Masaryk University, Czech Republic
Anirban Mukhopadhyay Technische Universität Darmstadt, Germany
Jack Noble Vanderbilt University, USA
Dzung Pham Henry Jackson Foundation, USA
David Svoboda Masaryk University, Czech Republic
Vladimír Ulman VSB - Technical University of Ostrava,
Czech Republic
Devrim Unay Izmir Demokrasi University, Turkey
François Varray Creatis, France
Jelmer M. Wolterink University of Twente, The Netherlands
Can Zhao NVIDIA, Johns Hopkins University, USA
Ting Zhao HHMI, Janelia Research Campus, USA
Contents
Contrast Adaptive Tissue Classification by Alternating Segmentation
and Synthesis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Dzung L. Pham, Yi-Yu Chou, Blake E. Dewey, Daniel S. Reich,
John A. Butman, and Snehashis Roy
3D Brain MRI GAN-Based Synthesis Conditioned on Partial
Volume Maps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Filip Rusak, Rodrigo Santa Cruz, Pierrick Bourgeat, Clinton Fookes,
Jurgen Fripp, Andrew Bradley, and Olivier Salvado
Synthesizing Realistic Brain MR Images with Noise Control . . . . . . . . . . . . 21
Lianrui Zuo, Blake E. Dewey, Aaron Carass, Yufan He, Muhan Shao,
Jacob C. Reinhold, and Jerry L. Prince
Simulated Diffusion Weighted Images Based on Model-Predicted
Tumor Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Pamela R. Jackson, Andrea Hawkins-Daarud, and Kristin R. Swanson
Blind MRI Brain Lesion Inpainting Using Deep Learning . . . . . . . . . . . . . . 41
José V. Manjón, José E. Romero, Roberto Vivo-Hernando,
Gregorio Rubio, Fernando Aparici, Maria de la Iglesia-Vaya,
Thomas Tourdias, and Pierrick Coupé
High-Quality Interpolation of Breast DCE-MRI Using
Learned Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Hongyu Wang, Jun Feng, Xiaoying Pan, Di Yang, and Baoying Chen
A Method for Tumor Treating Fields Fast Estimation . . . . . . . . . . . . . . . . . 60
Reuben R. Shamir and Zeev Bomzon
Heterogeneous Virtual Population of Simulated CMR Images
for Improving the Generalization of Cardiac Segmentation Algorithms. . . . . . 68
Yasmina Al Khalil, Sina Amirrajab, Cristian Lorenz, Jürgen Weese,
and Marcel Breeuwer
DyeFreeNet: Deep Virtual Contrast CT Synthesis . . . . . . . . . . . . . . . . . . . . 80
Jingya Liu, Yingli Tian, A. Muhteşem Ağıldere, K. Murat Haberal,
Mehmet Coşkun, Cihan Duzgol, and Oguz Akin
x Contents
A Gaussian Process Model Based Generative Framework for Data
Augmentation of Multi-modal 3D Image Volumes . . . . . . . . . . . . . . . . . . . 90
Nicolas H. Nbonsou Tegang, Jean-Rassaire Fouefack,
Bhushan Borotikar, Valérie Burdin, Tania S. Douglas,
and Tinashe E. M. Mutsvangwa
Frequency-Selective Learning for CT to MR Synthesis . . . . . . . . . . . . . . . . 101
Zi Lin, Manli Zhong, Xiangzhu Zeng, and Chuyang Ye
Uncertainty-Aware Multi-resolution Whole-Body MR to CT Synthesis . . . . . 110
Kerstin Kläser, Pedro Borges, Richard Shaw, Marta Ranzini,
Marc Modat, David Atkinson, Kris Thielemans, Brian Hutton,
Vicky Goh, Gary Cook, M. Jorge Cardoso, and Sébastien Ourselin
UltraGAN: Ultrasound Enhancement Through Adversarial Generation. . . . . . 120
Maria Escobar, Angela Castillo, Andrés Romero, and Pablo Arbeláez
Improving Endoscopic Decision Support Systems by Translating Between
Imaging Modalities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
GeorgWimmer,MichaelGadermayr,AndreasVécsei,andAndreasUhl
An Unsupervised Adversarial Learning Approach to Fundus Fluorescein
Angiography Image Synthesis for Leakage Detection. . . . . . . . . . . . . . . . . . 142
WanyueLi,YiHe,JingWang,WenKong,YiweiChen,andGuoHuaShi
Towards Automatic Embryo Staging in 3D+t Microscopy Images Using
Convolutional Neural Networks and PointNets . . . . . . . . . . . . . . . . . . . . . . 153
Manuel Traub and Johannes Stegmaier
Train Small, Generate Big: Synthesis of Colorectal Cancer
Histology Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
Srijay Deshpande, Fayyaz Minhas, and Nasir Rajpoot
Image Synthesis as a Pretext for Unsupervised
Histopathological Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Dejan Štepec and Danijel Skočaj
Auditory Nerve Fiber Health Estimation Using Patient Specific Cochlear
Implant Stimulation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
Ziteng Liu, Ahmet Cakir, and Jack H. Noble
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195