Yinghuan Shi · Heung-Il SukMingxia Liu (Eds.)

123

LNCS

110

46

9th International Workshop, MLMI 2018 Held in Conjunction with MICCAI 2018 Granada, Spain, September 16, 2018, Proceedings

Machine Learning in Medical Imaging

Lecture Notes in Computer Science 11046Commenced Publication in 1973Founding and Former Series Editors:Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen

Editorial Board

David HutchisonLancaster University, Lancaster, UK

Takeo KanadeCarnegie Mellon University, Pittsburgh, PA, USA

Josef KittlerUniversity of Surrey, Guildford, UK

Jon M. KleinbergCornell University, Ithaca, NY, USA

Friedemann MatternETH Zurich, Zurich, Switzerland

John C. MitchellStanford University, Stanford, CA, USA

Moni NaorWeizmann Institute of Science, Rehovot, Israel

C. Pandu RanganIndian Institute of Technology Madras, Chennai, India

Bernhard SteffenTU Dortmund University, Dortmund, Germany

Demetri TerzopoulosUniversity of California, Los Angeles, CA, USA

Doug TygarUniversity of California, Berkeley, CA, USA

Gerhard WeikumMax Planck Institute for Informatics, Saarbrücken, Germany

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More information about this series at http://www.springer.com/series/7411

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http://www.springer.com/series/7411

Yinghuan Shi • Heung-Il SukMingxia Liu (Eds.)

Machine Learningin Medical Imaging9th International Workshop, MLMI 2018Held in Conjunction with MICCAI 2018Granada, Spain, September 16, 2018Proceedings

123

mingxia_liu@med.unc.edu

EditorsYinghuan ShiNanjing UniversityNanjingChina

Heung-Il SukKorea UniversitySeoulKorea (Republic of)

Mingxia LiuUniversity of North Carolina at Chapel HillChapel Hill, NCUSA

ISSN 0302-9743 ISSN 1611-3349 (electronic)Lecture Notes in Computer ScienceISBN 978-3-030-00918-2 ISBN 978-3-030-00919-9 (eBook)https://doi.org/10.1007/978-3-030-00919-9

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Preface

The 9th International Workshop on Machine Learning in Medical Imaging (MLMI2018) was held in Granada, Spain, on September 16, 2018, in conjunction with the 21stInternational Conference on Medical Image Computing and Computer-AssistedIntervention (MICCAI).

In recent years, machine learning is playing an essential role in the medical imagingfield, including computer-assisted diagnosis, image segmentation, image registration,image fusion, image-guided therapy, image annotation, and image database retrieval.With advances in medical imaging, new imaging modalities and methodologies, as wellas new machine learning algorithms/applications, are taking center stage in medicalimaging. Owing to large inter-subject variations and complexities, it is generally dif-ficult to derive analytic formulations or simple equations to represent objects such aslesions and anatomy in medical images. Therefore, tasks in medical imaging requirelearning from patient data for heuristics and prior knowledge, in order to facilitate thedetection/diagnosis of abnormalities in medical images.

The main aim of the MLMI 2018 workshop was to help advance scientific researchwithin the broad field of machine learning in medical imaging. The workshop focusedon major trends and challenges in this area, and presented works aimed to identify newcutting-edge techniques and their use in medical imaging. We hope that the MLMIworkshop becomes an important platform for translating research from the bench to thebedside.

The range and level of submissions for this year’s meeting were of very highquality. Authors were asked to submit full-length papers for review. A total of 82papers were submitted to the workshop in response to the call for papers. Each of the82 papers underwent a rigorous double-blinded peer-review process, with each paperbeing reviewed by at least two reviewers from the Program Committee, composed of50 well-known experts in the field. Based on the reviewing scores and critiques, the 46best papers (56%) were accepted for presentation at the workshop and chosen to beincluded in this Springer LNCS volume. The large variety of machine-learning tech-niques applied to medical imaging were well represented at the workshop.

We are grateful to the Program Committee for reviewing the submitted papers andgiving constructive comments and critiques, to the authors for submitting high-qualitypapers, to the presenters for excellent presentations, and to all the MLMI 2018attendees coming to Granada from all around the world.

August 2018 Yinghuan ShiHeung-Il SukMingxia Liu

mingxia_liu@med.unc.edu

Organization

Workshop Organizers

Yinghuan Shi Nanjing University, ChinaHeung-Il Suk Korea University, Republic of KoreaMingxia Liu University of North Carolina at Chapel Hill, USA

Steering Committee

Dinggang Shen University of North Carolina at Chapel Hill, USAPingkun Yan Philips Research North America, USAKenji Suzuki Illinois Institute of Technology, USA and World

Research Hub Initiative, Tokyo Institute ofTechnology, Japan

Fei Wang AliveCor Inc., USA

Program Committee

Amin Zarshenas Illinois Institute of Technology, USAAntonios Makropoulos Imperial College London, UKChunfeng Lian University of North Carolina at Chapel Hill, USAFrancesco Ciompi Radboud University Medical Center, The NetherlandsGang Li University of North Carolina at Chapel Hill, USAGerard Sanrom Pompeu Fabra University, SpainGhassan Hamarneh Simon Fraser University, CanadaGuoyan Zheng University of Bern, SwitzerlandHanbo Chen University of Georgia, USAHeang-Ping Chan University of Michigan Medical Center, USAHolger Roth NVIDIAHoo-Chang Shin National Institutes of Health, USAJaeil Kim Kyungpook National University, Republic of KoreaJanne Nappi Massachusetts General Hospital, USAJong-Hwan Lee Korea University, Republic of KoreaJun Zhang University of North Carolina at Chapel Hill, USAJunchi Liu Illinois Institute of Technology, USAJurgen Fripp Australian e-Health Research CentreKelei He Nanjing University, ChinaKen’ichi Morooka Kyushu University, JapanKilian Pohl SRI International, USAKim-Han Thung University of North Carolina at Chapel Hill, USALi Shen University of Pennsylvania, USALi Wang University of North Carolina at Chapel Hill, USA

mingxia_liu@med.unc.edu

Liang Sun Nanjing University of Aeronautics and Astronautics,China

Luping Zhou University of Wollongong, AustraliaMasahiro Oda Nagoya University, JapanMingliang Wang Nanjing University of Aeronautics and Astronautics,

ChinaMyung-Cheol Roh KakaocorpPhilip Ogunbona University of Wollongong, AustraliaPinzhuo Tian Nanjing University, ChinaTae-Eui Kam University of North Carolina at Chapel Hill, USATao Zhou Shanghai Jiao Tong University, ChinaQian Wang Shanghai Jiao Tong University, ChinaQian Yu Nanjing University, ChinaSanghyun Park DGIST, Republic of KoreaShaoting Zhang University of North Carolina at Charlotte, USAShihui Ying Shanghai University, ChinaShuai Wang University of North Carolina at Chapel Hill, USASihang Zhou National University of Defense Technology, ChinaWanqi Yang Nanjing Normal University, ChinaWenjia Bai Imperial College London, UKXi Jiang University of Georgia, USAXiang Li University of Georgia, USAXiaoyu Ding National Institute of Health, USAYanrong Guo Hefei University of Technology, ChinaYaozong Gao United Imaging, ChinaYasushi Hirano Yamaguchi University, JapanYongsheng Pan University of North Carolina at Chapel Hill, USAYuanjie Zheng University of Pennsylvania, USA

VIII Organization

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Contents

Developing Novel Weighted Correlation Kernels for Convolutional NeuralNetworks to Extract Hierarchical Functional Connectivities from fMRIfor Disease Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Biao Jie, Mingxia Liu, Chunfeng Lian, Feng Shi, and Dinggang Shen

Robust Contextual Bandit via the Capped-‘2 Norm for Mobile HealthIntervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Feiyun Zhu, Xinliang Zhu, Sheng Wang, Jiawen Yao, Zhichun Xiao,and Junzhou Huang

Dynamic Multi-scale CNN Forest Learning for Automatic CervicalCancer Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Nesrine Bnouni, Islem Rekik, Mohamed Salah Rhim,and Najoua Essoukri Ben Amara

Multi-task Fundus Image Quality Assessment via Transfer Learningand Landmarks Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Yaxin Shen, Ruogu Fang, Bin Sheng, Ling Dai, Huating Li, Jing Qin,Qiang Wu, and Weiping Jia

End-to-End Lung Nodule Detection in Computed Tomography. . . . . . . . . . . 37Dufan Wu, Kyungsang Kim, Bin Dong, Georges El Fakhri,and Quanzheng Li

CT Image Enhancement Using Stacked Generative Adversarial Networksand Transfer Learning for Lesion Segmentation Improvement. . . . . . . . . . . . 46

Youbao Tang, Jinzheng Cai, Le Lu, Adam P. Harrison, Ke Yan,Jing Xiao, Lin Yang, and Ronald M. Summers

Deep Learning Based Inter-modality Image Registration Supervisedby Intra-modality Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Xiaohuan Cao, Jianhuan Yang, Li Wang, Zhong Xue, Qian Wang,and Dinggang Shen

Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Jun-Sik Choi, Eunho Lee, and Heung-Il Suk

Joint Registration And Segmentation Of Xray Images Using GenerativeAdversarial Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Dwarikanath Mahapatra, Zongyuan Ge, Suman Sedai,and Rajib Chakravorty

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SCCA-Ref: Novel Sparse Canonical Correlation Analysis with Referenceto Discover Independent Spatial Associations Between White MatterHyperintensities and Atrophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Gerard Sanroma, Loes Rutten-Jacobs, Valerie Lohner,Johanna Kramme, Sach Mukherjee, Martin Reuter, Tony Stoecker,and Monique M. B. Breteler

Synthesizing Dynamic MRI Using Long-Term RecurrentConvolutional Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Frank Preiswerk, Cheng-Chieh Cheng, Jie Luo, and Bruno Madore

Automatically Designing CNN Architectures for Medical ImageSegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Aliasghar Mortazi and Ulas Bagci

Rotation Invariance and Directional Sensitivity: Spherical Harmonicsversus Radiomics Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Adrien Depeursinge, Julien Fageot, Vincent Andrearczyk,John Paul Ward, and Michael Unser

Can Dilated Convolutions Capture Ultrasound Video Dynamics? . . . . . . . . . 116Mohammad Ali Maraci, Weidi Xie, and J. Alison Noble

Topological Correction of Infant Cortical Surfaces Using AnatomicallyConstrained U-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

Liang Sun, Daoqiang Zhang, Li Wang, Wei Shao, Zengsi Chen,Weili Lin, Dinggang Shen, and Gang Li

Self-taught Learning with Residual Sparse Autoencoders for HEp-2 CellStaining Pattern Recognition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Xian-Hua Han, JiandDe Sun, Lanfen Lin, and Yen-Wei Chen

Semantic-Aware Generative Adversarial Nets for Unsupervised DomainAdaptation in Chest X-Ray Segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . 143

Cheng Chen, Qi Dou, Hao Chen, and Pheng-Ann Heng

Brain Status Prediction with Non-negative Projective Dictionary Learning . . . 152Mingli Zhang, Christian Desrosiers, Yuhong Guo, Caiming Zhang,Budhachandra Khundrakpam, and Alan Evans

Classification of Pancreatic Cystic Neoplasms Based on MultimodalityImages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Weixiang Chen, Hongchen Ji, Jianjiang Feng, Rong Liu,Yi Yu, Ruiquan Zhou, and Jie Zhou

X Contents

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Retinal Blood Vessel Segmentation Using a Fully ConvolutionalNetwork – Transfer Learning from Patch- to Image-Level . . . . . . . . . . . . . . 170

Taibou Birgui Sekou, Moncef Hidane, Julien Olivier, and Hubert Cardot

Combining Deep Learning and Active Contours Opens The Way to Robust,Automated Analysis of Brain Cytoarchitectonics . . . . . . . . . . . . . . . . . . . . . 179

Konstantin Thierbach, Pierre-Louis Bazin, Walter de Back,Filippos Gavriilidis, Evgeniya Kirilina, Carsten Jäger,Markus Morawski, Stefan Geyer, Nikolaus Weiskopf, and Nico Scherf

Latent3DU-net: Multi-level Latent Shape Space Constrained 3D U-net forAutomatic Segmentation of the Proximal Femur from Radial MRIof the Hip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

Guodong Zeng, Qian Wang, Till Lerch, Florian Schmaranzer,Moritz Tannast, Klaus Siebenrock, and Guoyan Zheng

Adversarial Image Registration with Application for MR and TRUS ImageFusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

Pingkun Yan, Sheng Xu, Ardeshir R. Rastinehad, and Brad J. Wood

Reproducible White Matter Tract Segmentation Using 3D U-Neton a Large-scale DTI Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

Bo Li, Marius de Groot, Meike W. Vernooij, M. Arfan Ikram,Wiro J. Niessen, and Esther E. Bron

Competition vs. Concatenation in Skip Connections of Fully ConvolutionalNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214

Santiago Estrada, Sailesh Conjeti, Muneer Ahmad, Nassir Navab,and Martin Reuter

Ensemble of Multi-sized FCNs to Improve White Matter LesionSegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Zhewei Wang, Charles D. Smith, and Jundong Liu

Automatic Accurate Infant Cerebellar Tissue Segmentation with DenselyConnected Convolutional Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

Jiawei Chen, Han Zhang, Dong Nie, Li Wang, Gang Li, Weili Lin,and Dinggang Shen

Nuclei Detection Using Mixture Density Networks . . . . . . . . . . . . . . . . . . . 241Navid Alemi Koohababni, Mostafa Jahanifar, Ali Gooya, and NasirRajpoot

Attention-Guided Curriculum Learning for Weakly SupervisedClassification and Localization of Thoracic Diseases onChest Radiographs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

Yuxing Tang, Xiaosong Wang, Adam P. Harrison, Le Lu, Jing Xiao,and Ronald M. Summers

Contents XI

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Graph of Hippocampal Subfields Grading for Alzheimer’s DiseasePrediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259

Kilian Hett, Vinh-Thong Ta, José V. Manjón, and Pierrick Coupé

Deep Multiscale Convolutional Feature Learning for Weakly SupervisedLocalization of Chest Pathologies in X-ray Images . . . . . . . . . . . . . . . . . . . 267

Suman Sedai, Dwarikanath Mahapatra, Zongyuan Ge,Rajib Chakravorty, and Rahil Garnavi

Combining Heterogeneously Labeled Datasets For Training SegmentationNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276

Jana Kemnitz, Christian F. Baumgartner, Wolfgang Wirth,Felix Eckstein, Sebastian K. Eder, and Ender Konukoglu

SoLiD: Segmentation of Clostridioides Difficile Cells in the Presenceof Inhomogeneous Illumination Using a Deep Adversarial Network. . . . . . . . 285

Ali Memariani and Ioannis A. Kakadiaris

On the Adaptability of Unsupervised CNN-Based Deformable ImageRegistration to Unseen Image Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . 294

Enzo Ferrante, Ozan Oktay, Ben Glocker, and Diego H. Milone

Early Diagnosis of Autism Disease by Multi-channel CNNs . . . . . . . . . . . . . 303Guannan Li, Mingxia Liu, Quansen Sun, Dinggang Shen,and Li Wang

Longitudinal and Multi-modal Data Learning via Joint Embedding andSparse Regression for Parkinson’s Disease Diagnosis . . . . . . . . . . . . . . . . . 310

Haijun Lei, Zhongwei Huang, Ahmed Elazab, Hancong Li,and Baiying Lei

Prostate Cancer Classification on VERDICT DW-MRI UsingConvolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319

Eleni Chiou, Francesco Giganti, Elisenda Bonet-Carne,Shonit Punwani, Iasonas Kokkinos, and Eleftheria Panagiotaki

Detection of the Pharyngeal Phase in the Videofluoroscopic SwallowingStudy Using Inflated 3D Convolutional Networks . . . . . . . . . . . . . . . . . . . . 328

Jong Taek Lee and Eunhee Park

End-To-End Alzheimer’s Disease Diagnosis and Biomarker Identification . . . 337Soheil Esmaeilzadeh, Dimitrios Ioannis Belivanis, Kilian M. Pohl,and Ehsan Adeli

Small Organ Segmentation in Whole-Body MRI Using a Two-Stage FCNand Weighting Schemes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346

Vanya V. Valindria, Ioannis Lavdas, Juan Cerrolaza, Eric O. Aboagye,Andrea G. Rockall, Daniel Rueckert, and Ben Glocker

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Masseter Segmentation from Computed Tomography UsingFeature-Enhanced Nested Residual Neural Network. . . . . . . . . . . . . . . . . . . 355

Haifang Qin, Yuru Pei, Yuke Guo, Gengyu Ma, Tianmin Xu,and Hongbin Zha

Iterative Interaction Training for Segmentation Editing Networks . . . . . . . . . 363Gustav Bredell, Christine Tanner, and Ender Konukoglu

Temporal Consistent 2D-3D Registration of Lateral Cephalogramsand Cone-Beam Computed Tomography Images . . . . . . . . . . . . . . . . . . . . . 371

Yungeng Zhang, Yuru Pei, Haifang Qin, Yuke Guo, Gengyu Ma,Tianmin Xu, and Hongbin Zha

Computation of Total Kidney Volume from CT Images in AutosomalDominant Polycystic Kidney Disease Using Multi-task 3D ConvolutionalNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380

Deepak Keshwani, Yoshiro Kitamura, and Yuanzhong Li

Dynamic Routing on Deep Neural Network for Thoracic DiseaseClassification and Sensitive Area Localization . . . . . . . . . . . . . . . . . . . . . . 389

Yan Shen and Mingchen Gao

Deep Learning for Fast and Spatially-Constrained Tissue Quantificationfrom Highly-Undersampled Data in Magnetic Resonance Fingerprinting(MRF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398

Zhenghan Fang, Yong Chen, Mingxia Liu, Yiqiang Zhan, Weili Lin,and Dinggang Shen

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407

Contents XIII

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Author Index

Aboagye, Eric O. 346Adeli, Ehsan 337Ahmad, Muneer 214Amara, Najoua Essoukri Ben 19Andrearczyk, Vincent 107

Back, Walter de 179Bagci, Ulas 98Baumgartner, Christian F. 276Bazin, Pierre-Louis 179Belivanis, Dimitrios Ioannis 337Birgui Sekou, Taibou 170Bnouni, Nesrine 19Bonet-Carne, Elisenda 319Bredell, Gustav 363Breteler, Monique M. B. 81Bron, Esther E. 205

Cai, Jinzheng 46Cao, Xiaohuan 55Cardot, Hubert 170Cerrolaza, Juan 346Chakravorty, Rajib 73, 267Chen, Cheng 143Chen, Hao 143Chen, Jiawei 233Chen, Weixiang 161Chen, Yen-Wei 134Chen, Yong 398Chen, Zengsi 125Cheng, Cheng-Chieh 89Chiou, Eleni 319Choi, Jun-Sik 64Conjeti, Sailesh 214Coupé, Pierrick 259

Dai, Ling 28de Groot, Marius 205Depeursinge, Adrien 107Desrosiers, Christian 152

Dong, Bin 37Dou, Qi 143

Eckstein, Felix 276Eder, Sebastian K. 276Elazab, Ahmed 310Esmaeilzadeh, Soheil 337Estrada, Santiago 214Evans, Alan 152

Fageot, Julien 107Fakhri, Georges El 37Fang, Ruogu 28Fang, Zhenghan 398Feng, Jianjiang 161Ferrante, Enzo 294

Gao, Mingchen 389Garnavi, Rahil 267Gavriilidis, Filippos 179Ge, Zongyuan 73, 267Geyer, Stefan 179Giganti, Francesco 319Glocker, Ben 294, 346Gooya, Ali 241Guo, Yuhong 152Guo, Yuke 355, 371

Han, Xian-Hua 134Harrison, Adam P. 46, 249Heng, Pheng-Ann 143Hett, Kilian 259Hidane, Moncef 170Huang, Junzhou 10Huang, Zhongwei 310

Ikram, M. Arfan 205

Jäger, Carsten 179Jahanifar, Mostafa 241

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Ji, Hongchen 161Jia, Weiping 28Jie, Biao 1

Kakadiaris, Ioannis A. 285Kemnitz, Jana 276Keshwani, Deepak 380Khundrakpam, Budhachandra 152Kim, Kyungsang 37Kirilina, Evgeniya 179Kitamura, Yoshiro 380Kokkinos, Iasonas 319Konukoglu, Ender 276, 363Koohababni, Navid Alemi 241Kramme, Johanna 81

Lavdas, Ioannis 346Lee, Eunho 64Lee, Jong Taek 328Lei, Baiying 310Lei, Haijun 310Lerch, Till 188Li, Bo 205Li, Gang 125, 233Li, Guannan 303Li, Hancong 310Li, Huating 28Li, Quanzheng 37Li, Yuanzhong 380Lian, Chunfeng 1Lin, Lanfen 134Lin, Weili 125, 233, 398Liu, Jundong 223Liu, Mingxia 1, 303, 398Liu, Rong 161Lohner, Valerie 81Lu, Le 46, 249Luo, Jie 89

Ma, Gengyu 355, 371Madore, Bruno 89Mahapatra, Dwarikanath 73, 267Manjón, José V. 259Maraci, Mohammad Ali 116Memariani, Ali 285Milone, Diego H. 294Morawski, Markus 179Mortazi, Aliasghar 98Mukherjee, Sach 81

Navab, Nassir 214Nie, Dong 233Niessen, Wiro J. 205Noble, J. Alison 116

Oktay, Ozan 294Olivier, Julien 170

Panagiotaki, Eleftheria 319Park, Eunhee 328Pei, Yuru 355, 371Pohl, Kilian M. 337Preiswerk, Frank 89Punwani, Shonit 319

Qin, Haifang 355, 371Qin, Jing 28

Rajpoot, Nasir 241Rastinehad, Ardeshir R. 197Rekik, Islem 19Reuter, Martin 81, 214Rhim, Mohamed Salah 19Rockall, Andrea G. 346Rueckert, Daniel 346Rutten-Jacobs, Loes 81

Sanroma, Gerard 81Scherf, Nico 179Schmaranzer, Florian 188Sedai, Suman 73, 267Shao, Wei 125Shen, Dinggang 1, 55, 125, 233, 303, 398Shen, Yan 389Shen, Yaxin 28Sheng, Bin 28Shi, Feng 1Siebenrock, Klaus 188Smith, Charles D. 223Stoecker, Tony 81Suk, Heung-Il 64Summers, Ronald M. 46, 249Sun, JiandDe 134Sun, Liang 125Sun, Quansen 303

Ta, Vinh-Thong 259Tang, Youbao 46Tang, Yuxing 249

408 Author Index

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Tannast, Moritz 188Tanner, Christine 363Thierbach, Konstantin 179

Unser, Michael 107

Valindria, Vanya V. 346Vernooij, Meike W. 205

Wang, Li 55, 125, 233, 303Wang, Qian 55, 188Wang, Sheng 10Wang, Xiaosong 249Wang, Zhewei 223Ward, John Paul 107Weiskopf, Nikolaus 179Wirth, Wolfgang 276Wood, Brad J. 197Wu, Dufan 37Wu, Qiang 28

Xiao, Jing 46, 249Xiao, Zhichun 10Xie, Weidi 116

Xu, Sheng 197Xu, Tianmin 355, 371Xue, Zhong 55

Yan, Ke 46Yan, Pingkun 197Yang, Jianhuan 55Yang, Lin 46Yao, Jiawen 10Yu, Yi 161

Zeng, Guodong 188Zha, Hongbin 355, 371Zhan, Yiqiang 398Zhang, Caiming 152Zhang, Daoqiang 125Zhang, Han 233Zhang, Mingli 152Zhang, Yungeng 371Zheng, Guoyan 188Zhou, Jie 161Zhou, Ruiquan 161Zhu, Feiyun 10Zhu, Xinliang 10

Author Index 409

mingxia_liu@med.unc.edu

PrefaceOrganizationContentsDeveloping Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis1 Introduction2 Method2.1 Subjects and Image Preprocessing2.2 Proposed Weighted Correlation Kernel2.3 Architecture of the Proposed Wc-Kernel Based CNN

3 Experiments4 ConclusionReferences

Robust Contextual Bandit via the Capped-2 Norm for Mobile Health Intervention1 Introduction2 Preliminaries3 Robust Contextual Bandit with Capped-2 Norm3.1 Algorithm for the Critic Updating3.2 Algorithm for the Actor Updating

4 Experiments4.1 Datasets4.2 Experiments Settings4.3 Results and Discussion

5 Conclusions and Future DirectionsReferences

Dynamic Multi-scale CNN Forest Learning for Automatic Cervical Cancer SegmentationAbstract1 Introduction2 Proposed Cluster-Based Dynamic Multi-scale Dynamic Forest2.1 Root Node CNN Architecture2.2 Cascaded CNNs2.3 Proposed CNN-Based Dynamic Multi-scale Tree (DMT)2.4 Proposed CK+1DMF Learning Framework

3 Results and Discussion4 ConclusionReferences

Multi-task Fundus Image Quality Assessment via Transfer Learning and Landmarks Detection1 Introduction2 Dataset3 Method3.1 Multi-task Convolutional Neural Networks3.2 Optic Disc and Fovea Detection3.3 Learning

4 Experiments and Results5 ConclusionReferences

End-to-End Lung Nodule Detection in Computed TomographyAbstract1 Introduction2 Methodology2.1 Overview2.2 Reconstruction Sub-network2.3 Detection Sub-network2.4 End-to-End Fine Tuning2.5 Inference

3 Simulation Setup3.1 Data Source3.2 Training Parameters3.3 Evaluation

4 Results4.1 FROC Analysis4.2 Reconstructed Images

5 Conclusion and DiscussionReferences

CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement1 Introduction2 Methods2.1 CT Image Enhancement2.2 Lesion Segmentation

3 Experimental Results and Analyses4 ConclusionsReferences

Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality SimilarityAbstract1 Introduction2 Method2.1 Loss Function Based on Intra-modality Similarity2.2 Inter-modality Registration Network2.3 Spatial Transformation Layer

3 Experimental Results3.1 Registration Results

4 ConclusionReferences

Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis1 Introduction2 Materials and Preprocessing3 Proposed Method3.1 Regional Abnormality Representation3.2 Brain-Wise Feature Extraction and Classifier Learning

4 Experimental Settings and Results4.1 Experimental Settings4.2 Results and Discussion

5 ConclusionReferences

Joint Registration And Segmentation Of Xray Images Using Generative Adversarial Networks1 Introduction2 Methods2.1 Joint Registration and Segmentation Using GANs2.2 Deformation Field Consistency2.3 Obtaining Segmentation Mask

3 Experiments3.1 Results on NIH dataset

4 ConclusionReferences

SCCA-Ref: Novel Sparse Canonical Correlation Analysis with Reference to Discover Independent Spatial Associations Between White Matter Hyperintensities and Atrophy1 Introduction2 Method2.1 Classical CCA for Joint Analysis of WMH and Atrophy2.2 SCCA-Ref2.3 Regional WMH Burden

3 Experiments4 ConclusionsReferences

Synthesizing Dynamic MRI Using Long-Term Recurrent Convolutional Networks1 Introduction2 Materials and Methods2.1 Network Architecture

3 Results and DiscussionReferences

Automatically Designing CNN Architectures for Medical Image Segmentation1 Introduction2 Methods2.1 Policy Gradient2.2 Proposed Base-Architecture for Image Segmentation2.3 Learnable Hyperparameters

3 Experiments and Results4 Discussions and ConclusionReferences

Rotation Invariance and Directional Sensitivity: Spherical Harmonics versus Radiomics Features1 Introduction2 Materials and Methods2.1 Notations2.2 Local Rotation Invariance and Directional Sensitivity2.3 Spherical Harmonic Wavelets

3 Results3.1 LRI and DS of Popular Radiomics Operators3.2 3D Synthetic Texture Classification

4 ConclusionsReferences

Can Dilated Convolutions Capture Ultrasound Video Dynamics?1 Introduction2 Materials and Methods2.1 Data and Experimental Setup2.2 Network Architecture2.3 Attention Mask Module

3 Results4 Discussion and ConclusionReferences

Topological Correction of Infant Cortical Surfaces Using Anatomically Constrained U-Net1 Introduction2 Method2.1 Training Set Construction2.2 Anatomically Constrained U-Net2.3 Inferring the New Labels of Candidate Voxels

3 Experiments3.1 Dataset and Experimental Settings3.2 Result

4 ConclusionReferences

Self-taught Learning with Residual Sparse Autoencoders for HEp-2 Cell Staining Pattern Recognition1 Introduction2 Method2.1 Material2.2 Residual SAE for Self-taught Learning2.3 The Aggregated Activation of the Residual SAE for Image Representation

3 Experimental Results4 ConclusionsReferences

Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-Ray Segmentation1 Introduction2 Method2.1 Segmentation Network Established on Source Domain2.2 Image Transformation with Semantic-Aware GANs2.3 Learning Procedure and Implementation Details

3 Experimental Results4 ConclusionReferences

Brain Status Prediction with Non-negative Projective Dictionary Learning1 Introduction2 The Proposed Approach2.1 Non-negative Projective Dictionary Learning2.2 Training Algorithm2.3 Classification2.4 Complexity and Convergence

3 Experiments3.1 Prediction of MCI-to-AD Conversion3.2 Prediction of Brain Age

4 ConclusionReferences

Classification of Pancreatic Cystic Neoplasms Based on Multimodality Images1 Introduction2 Methodology2.1 Framework of PCN-Net2.2 First Stage: Feature Extraction and Region Proposal2.3 Second Stage: Intra-modality Localization and Inter-modality Registration2.4 Third Stage: Modality Fusion and Classification

3 Experiment3.1 Dataset and Annotation3.2 Implementation Details3.3 Result and Comparisons

4 Discussion and ConclusionReferences

Retinal Blood Vessel Segmentation Using a Fully Convolutional Network – Transfer Learning from Patch- to Image-Level1 Introduction2 Proposed Model2.1 Fully Convolutional Networks and Transfer Learning2.2 The Proposed Framework2.3 Neural Network Architecture

3 Experiments3.1 Data Preparation and Network Training3.2 Results3.3 Discussion

4 Summary and PerspectivesReferences

Combining Deep Learning and Active Contours Opens The Way to Robust, Automated Analysis of Brain Cytoarchitectonics1 Introduction2 Methodology2.1 Sample Preparation2.2 Image Data2.3 Cell Segmentation Workflow

3 Results4 ConclusionsReferences

Latent3DU-net: Multi-level Latent Shape Space Constrained 3D U-net for Automatic Segmentation of the Proximal Femur from Radial MRI of the Hip1 Introduction2 Methods2.1 Spatial Transform2.2 Segmentation of the Proximal Femur

3 Experiments and Results3.1 Dataset and Preprocessing3.2 Training3.3 Testing and Evaluation3.4 Results

4 ConclusionsReferences

Adversarial Image Registration with Application for MR and TRUS Image Fusion1 Introduction2 Adversarial Image Registration (AIR)2.1 Generator and Discriminator Networks2.2 Adversarial Training

3 Experiments3.1 Materials and Training3.2 Experimental Results

4 ConclusionsReferences

Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset1 Introduction2 Methods2.1 Model2.2 Dataset2.3 Preprocessing2.4 Reference Standard2.5 Evaluation Metrics

3 Experiments3.1 Method Optimization Experiments3.2 Validation Experiments

4 Results4.1 Method Optimization Results4.2 Validation Results

5 DiscussionReferences

Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks1 Introduction2 Methodology2.1 Local Competition - Competitive Dense Block2.2 Global Competition - Competitive Un-pooling Block (CUB)2.3 Competitive Dense Fully Convolutional Network- CDFNet

3 Results and Discussion4 ConclusionReferences

Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation1 Introduction2 Dice as Evaluation Metric and Objective Function: Issues3 Remedy: Two-Stage-Multi-sized FCNs and a New Activation Function4 Experiments4.1 Experimental Results

5 ConclusionsReferences

Automatic Accurate Infant Cerebellar Tissue Segmentation with Densely Connected Convolutional NetworkAbstract1 Introduction2 Method2.1 Dataset and Preprocessing2.2 Network Architecture Design2.3 Network Training

3 Experimental Results3.1 Performance on 12-Month-Old Subjects3.2 Performance on 6-Month-Old Infants

4 ConclusionsAcknowledgementsReferences

Nuclei Detection Using Mixture Density Networks1 Introduction2 Mixture Density Networks2.1 Extending MDN for Nuclei Detection

3 Experimental Results4 ConclusionReferences

Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs1 Introduction2 Method2.1 A CNN Based Classification and Localization Framework2.2 Disease Severity-Level Based Curriculum Learning2.3 Attention Guided Iterative Refinement

3 Experiments4 ConclusionReferences

Graph of Hippocampal Subfields Grading for Alzheimer's Disease Prediction1 Introduction2 Materials and Methods3 Results and Discussions4 ConclusionsReferences

Deep Multiscale Convolutional Feature Learning for Weakly Supervised Localization of Chest Pathologies in X-ray Images1 Introduction2 Methodology2.1 Classification-CNN2.2 Class Aware Training of Convolutional Features2.3 Pathology Localization by Attention CNN (A-CNN)

3 Experiments4 ConclusionReferences

Combining Heterogeneously Labeled Datasets For Training Segmentation Networks1 Introduction2 Methods2.1 Naive Masking2.2 Super Label Aware Crossentropy Loss

3 Experiments and Results3.1 Data3.2 Network Architecture and Training3.3 Evaluation3.4 Discussion and Conclusion

References

SoLiD: Segmentation of Clostridioides Difficile Cells in the Presence of Inhomogeneous Illumination Using a Deep Adversarial Network1 Introduction2 Methods3 Experimental Results4 ConclusionReferences

On the Adaptability of Unsupervised CNN-Based Deformable Image Registration to Unseen Image Domains1 Introduction2 Materials and Methods2.1 Datasets and Clinical Context2.2 Unsupervised CNN-Based Image Registration2.3 Fine-Tuning and One-Shot Learning in the Context of Unsupervised CNN-Based Image Registration

3 Results and Discussion4 Conclusions and Future WorksReferences

Early Diagnosis of Autism Disease by Multi-channel CNNsAbstract1 Introduction2 Materials and Methods3 Experiments and Results4 ConclusionAcknowledgmentsReferences

Longitudinal and Multi-modal Data Learning via Joint Embedding and Sparse Regression for Parkinson’s Disease DiagnosisAbstract1 Introduction2 Related Work3 Methodology3.1 System Overview3.2 Notations3.3 Proposed Method

4 Experiment4.1 Image Preprocessing4.2 Experimental Setting4.3 Classification Performance4.4 Regression Performance

5 ConclusionReferences

Prostate Cancer Classification on VERDICT DW-MRI Using Convolutional Neural Networks1 Introduction2 Methods2.1 VERDICT DW-MRI Data2.2 Fully Convolutional Neural Networks

3 Results4 ConclusionReferences

Detection of the Pharyngeal Phase in the Videofluoroscopic Swallowing Study Using Inflated 3D Convolutional Networks1 Introduction2 Methodology2.1 Dataset2.2 Generating Pharyngeal Phase Candidates Using Optical Flow2.3 Training Inflated 3D Convolutional Networks Using RGB/Optical Flow/Joint

3 Experimental Results4 ConclusionReferences

End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification1 Introduction2 Dataset and Preprocessing3 3D-CNN Training and Evaluation4 Experiment Results5 ConclusionReferences

Small Organ Segmentation in Whole-Body MRI Using a Two-Stage FCN and Weighting Schemes1 Introduction2 Materials and Methods2.1 Materials2.2 Two-Stage Network: A Coarse-to-Fine Approach

3 Experiments and Results4 Discussion and ConclusionReferences

Masseter Segmentation from Computed Tomography Using Feature-Enhanced Nested Residual Neural Network1 Introduction2 Methods2.1 Masseter Region Location2.2 Feature-Enhanced Nested Residual Neural Network

3 Experiments4 Discussion and ConclusionReferences

Iterative Interaction Training for Segmentation Editing Networks1 Introduction2 Methods3 Experiments and Results4 ConclusionsReferences

Temporal Consistent 2D-3D Registration of Lateral Cephalograms and Cone-Beam Computed Tomography Images1 Introduction2 Method2.1 CNN-Based 2D-3D Registration2.2 Temporal Consistent 2D-3D Registration

3 Experiments4 ConclusionReferences

Computation of Total Kidney Volume from CT Images in Autosomal Dominant Polycystic Kidney Disease Using Multi-task 3D Convolutional Neural NetworksAbstract1 Introduction2 Materials and Methods2.1 Dataset and Preprocessing2.2 Multi-task 3D Fully Convolutional Network for ADPK Segmentation2.3 Bootstrapping Cross Entropy Loss

3 Experiments and Training4 Results5 Conclusion and Future WorkAcknowledgementsReferences

Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization1 Introduction2 Methods2.1 1 1 Convolutional Capsule Layer

3 Experiment Results4 ConclusionReferences

Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF)Abstract1 Introduction2 Materials and Method2.1 Data Acquisition and Pre-processing2.2 Proposed U-Net Model

3 Experiments3.1 Experimental Settings3.2 Results

4 ConclusionReferences

Author Index