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ORCID22nd MICCAI 2019: Shenzhen, China
- Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali R. Khan:
Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 - 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part III. Lecture Notes in Computer Science 11766, Springer 2019, ISBN 978-3-030-32247-2
Neuroimage Reconstruction and Synthesis
- Yao Sui, Onur Afacan
, Ali Gholipour, Simon K. Warfield:
Isotropic MRI Super-Resolution Reconstruction with Multi-scale Gradient Field Prior. 3-11 - Zheng Li, Qingping Liu, Yiran Li, Qiu Ge, Yuanqi Shang, Donghui Song, Ze Wang, Jun Shi:
A Two-Stage Multi-loss Super-Resolution Network for Arterial Spin Labeling Magnetic Resonance Imaging. 12-20 - Jing Cheng, Haifeng Wang, Leslie Ying, Dong Liang:
Model Learning: Primal Dual Networks for Fast MR Imaging. 21-29 - Yanxia Chen, Taohui Xiao, Cheng Li, Qiegen Liu, Shanshan Wang:
Model-Based Convolutional De-Aliasing Network Learning for Parallel MR Imaging. 30-38 - Viswanath P. Sudarshan, Kratika Gupta, Gary F. Egan, Zhaolin Chen, Suyash P. Awate:
Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework. 39-47 - Zhiyuan Liu, Huai Chen, Huafeng Liu:
Deep Learning Based Framework for Direct Reconstruction of PET Images. 48-56 - Jo Schlemper, Seyed Sadegh Mohseni Salehi, Prantik Kundu, Carole Lazarus, Hadrien Dyvorne, Daniel Rueckert, Michal Sofka:
Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction. 57-64 - Kai Xuan, Dongming Wei, Dijia Wu, Zhong Xue, Yiqiang Zhan, Weiwu Yao, Qian Wang:
Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans Using Sparse Fidelity Loss and Adversarial Regularization. 65-73 - Prabhjot Kaur, Anil Kumar Sao:
Single Image Based Reconstruction of High Field-Like MR Images. 74-82 - Juan Liu, Kevin M. Koch:
Deep Gated Convolutional Neural Network for QSM Background Field Removal. 83-91 - Elisabeth Hoppe, Florian Thamm, Gregor Körzdörfer, Christopher Syben, Franziska Schirrmacher, Mathias Nittka, Josef Pfeuffer, Heiko Meyer, Andreas Maier:
RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting. 92-100 - Zhenghan Fang, Yong Chen, Dong Nie, Weili Lin, Dinggang Shen:
RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting. 101-109 - Pan Liu, Chao Li, Carola-Bibiane Schönlieb:
GANReDL: Medical Image Enhancement Using a Generative Adversarial Network with Real-Order Derivative Induced Loss Functions. 110-117 - Gihyun Kwon, Chihye Han, Daeshik Kim:
Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks. 118-126 - Mahmoud Mostapha, Juan Prieto, Veronica Murphy, Jessica B. Girault, Mark Foster, Ashley Rumple, Joseph Blocher, Weili Lin, Jed T. Elison, John H. Gilmore, Steven M. Pizer, Martin Styner:
Semi-supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control. 127-136 - Yongsheng Pan, Mingxia Liu, Chunfeng Lian, Yong Xia, Dinggang Shen:
Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-modal Neuroimages. 137-145 - Muhammad Febrian Rachmadi, Maria del C. Valdés Hernández, Stephen D. Makin, Joanna M. Wardlaw, Taku Komura:
Predicting the Evolution of White Matter Hyperintensities in Brain MRI Using Generative Adversarial Networks and Irregularity Map. 146-154 - Pu Huang, Dengwang Li, Zhicheng Jiao, Dongming Wei, Guoshi Li, Qian Wang, Han Zhang, Dinggang Shen:
CoCa-GAN: Common-Feature-Learning-Based Context-Aware Generative Adversarial Network for Glioma Grading. 155-163 - Daniele Ravì, Daniel C. Alexander, Neil P. Oxtoby:
Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression. 164-172
Neuroimage Segmentation
- Zhanghexuan Ji, Yan Shen, Chunwei Ma, Mingchen Gao:
Scribble-Based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation. 175-183 - Chen Chen, Xiaopeng Liu, Meng Ding, Junfeng Zheng, Jiangyun Li:
3D Dilated Multi-fiber Network for Real-Time Brain Tumor Segmentation in MRI. 184-192 - Yalong Liu, Jie Li, Ying Wang, Miaomiao Wang, Xianjun Li, Zhicheng Jiao, Jian Yang, Xingbo Gao:
Refined Segmentation R-CNN: A Two-Stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants. 193-201 - Zhipeng Ding, Xu Han, Marc Niethammer:
VoteNet: A Deep Learning Label Fusion Method for Multi-atlas Segmentation. 202-210 - Kai Wu, Bowen Du, Man Luo, Hongkai Wen, Yiran Shen, Jianfeng Feng:
Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning. 211-219 - Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup Kim:
Scalable Neural Architecture Search for 3D Medical Image Segmentation. 220-228 - Wenguang Yuan, Jia Wei, Jiabing Wang, Qianli Ma, Tolga Tasdizen:
Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation from Multimodal Unpaired Images. 229-237 - Haruki Imai, Samuel Matzek, Tung D. Le, Yasushi Negishi, Kiyokuni Kawachiya:
High Resolution Medical Image Segmentation Using Data-Swapping Method. 238-246 - Kehan Qi, Hao Yang, Cheng Li, Zaiyi Liu, Meiyun Wang, Qiegen Liu, Shanshan Wang:
X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-Range Dependencies. 247-255 - Yuan-Xing Zhao, Yan-Ming Zhang, Ming Song, Cheng-Lin Liu:
Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network. 256-265 - Hao Yang, Weijian Huang, Kehan Qi, Cheng Li, Xinfeng Liu, Meiyun Wang, Hairong Zheng, Shanshan Wang:
CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke. 266-274 - Qiaoying Huang, Xiao Chen, Dimitris N. Metaxas, Mariappan S. Nadar:
Brain Segmentation from k-Space with End-to-End Recurrent Attention Network. 275-283 - Nicola K. Dinsdale, Mark Jenkinson, Ana I. L. Namburete:
Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images. 284-291 - Yuan Liang, Weinan Song, J. P. Dym, Kun Wang, Lei He:
CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion. 292-300 - Jiong Wu, Yue Zhang, Xiaoying Tang:
A Joint 3D+2D Fully Convolutional Framework for Subcortical Segmentation. 301-309 - Théo Estienne, Maria Vakalopoulou, Stergios Christodoulidis, Enzo Battistella, Marvin Lerousseau, Alexandre Carre, Guillaume Klausner, Roger Sun, Charlotte Robert, Stavroula G. Mougiakakou, Nikos Paragios, Eric Deutsch:
U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets. 310-319 - Nadieh Khalili, Elise Turk, Majd Zreik, Max A. Viergever, Manon J. N. L. Benders, Ivana Isgum:
Generative Adversarial Network for Segmentation of Motion Affected Neonatal Brain MRI. 320-328 - Bowei Zhou, Li Chen, Zhao Wang:
Interactive Deep Editing Framework for Medical Image Segmentation. 329-337 - Huahong Zhang, Alessandra M. Valcarcel, Rohit Bakshi, Renxin Chu, Francesca Bagnato, Russell T. Shinohara, Kilian Hett, Ipek Oguz:
Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices. 338-346 - Long Xie, Jiancong Wang, Mengjin Dong, David A. Wolk, Paul A. Yushkevich:
Improving Multi-atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation. 347-355 - Adrian V. Dalca, Evan M. Yu, Polina Golland, Bruce Fischl, Mert R. Sabuncu, Juan Eugenio Iglesias:
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation. 356-365 - Antoine Legouhy, Olivier Commowick, François Rousseau, Christian Barillot:
Online Atlasing Using an Iterative Centroid. 366-374 - Chaoyue Liu, Guohao Dong, Muqing Lin, Yaoxian Zou, Tianzhu Liang, Xujin He, Zhijie Chen, Dong Ni, Yi Xiong, Lei Zhu:
ARS-Net: Adaptively Rectified Supervision Network for Automated 3D Ultrasound Image Segmentation. 375-383 - Robert Wright, Nicolas Toussaint, Alberto Gómez, Veronika A. M. Zimmer, Bishesh Khanal, Jacqueline Matthew, Emily Skelton, Bernhard Kainz, Daniel Rueckert, Joseph V. Hajnal, Julia A. Schnabel:
Complete Fetal Head Compounding from Multi-view 3D Ultrasound. 384-392 - Ken C. L. Wong, Mehdi Moradi:
SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation. 393-401 - Zeju Li, Konstantinos Kamnitsas, Ben Glocker:
Overfitting of Neural Nets Under Class Imbalance: Analysis and Improvements for Segmentation. 402-410 - Hang Zhang, Jinwei Zhang, Qihao Zhang, Jeremy Kim, Shun Zhang, Susan A. Gauthier, Pascal Spincemaille, Thanh D. Nguyen, Mert R. Sabuncu, Yi Wang:
RSANet: Recurrent Slice-Wise Attention Network for Multiple Sclerosis Lesion Segmentation. 411-419 - Hai Xu, Hongtao Xie, Yizhi Liu, Chuandong Cheng, Chaoshi Niu, Yongdong Zhang:
Deep Cascaded Attention Network for Multi-task Brain Tumor Segmentation. 420-428 - Robin Brügger, Christian F. Baumgartner, Ender Konukoglu:
A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation. 429-437 - Magdalini Paschali, Stefano Gasperini, Abhijit Guha Roy, Michael Y.-S. Fang, Nassir Navab:
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation. 438-446 - Cheng Chen, Qi Dou, Yueming Jin, Hao Chen, Jing Qin, Pheng-Ann Heng:
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion. 447-456 - Shuai Chen, Gerda Bortsova, Antonio García-Uceda Juárez, Gijs van Tulder, Marleen de Bruijne:
Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation. 457-465 - Pierrick Coupé, Boris Mansencal, Michaël Clément, Rémi Giraud, Baudouin Denis de Senneville, Vinh-Thong Ta, Vincent Lepetit, José V. Manjón:
AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation. 466-474 - Hoyt Patrick Taylor IV, Zhengwang Wu, Ye Wu, Dinggang Shen, Han Zhang, Pew-Thian Yap:
Automated Parcellation of the Cortex Using Structural Connectome Harmonics. 475-483 - Shuo Han, Aaron Carass, Jerry L. Prince:
Hierarchical Parcellation of the Cerebellum. 484-491 - Zhengwang Wu, Fenqiang Zhao, Jing Xia, Li Wang, Weili Lin, John H. Gilmore, Gang Li, Dinggang Shen:
Intrinsic Patch-Based Cortical Anatomical Parcellation Using Graph Convolutional Neural Network on Surface Manifold. 492-500 - Prasanna Parvathaneni, Shunxing Bao, Vishwesh Nath, Neil D. Woodward, Daniel O. Claassen, Carissa J. Cascio, David H. Zald, Yuankai Huo, Bennett A. Landman, Ilwoo Lyu:
Cortical Surface Parcellation Using Spherical Convolutional Neural Networks. 501-509 - Eytan Kats, Jacob Goldberger, Hayit Greenspan:
A Soft STAPLE Algorithm Combined with Anatomical Knowledge. 510-517
Diffusion-Weighted Magnetic Resonance Imaging
- Siyuan Liu, Kim-Han Thung, Weili Lin, Pew-Thian Yap, Dinggang Shen:
Multi-stage Image Quality Assessment of Diffusion MRI via Semi-supervised Nonlocal Residual Networks. 521-528 - Yoonmi Hong, Geng Chen, Pew-Thian Yap, Dinggang Shen:
Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks. 529-537 - Jin-Kyu Gahm, Yonggang Shi:
Surface-Based Tracking of U-Fibers in the Superficial White Matter. 538-546 - Khoi Minh Huynh, Tiantian Xu, Ye Wu, Geng Chen, Kim-Han Thung, Haiyong Wu, Weili Lin, Dinggang Shen, Pew-Thian Yap:
Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments. 547-555 - Khoi Minh Huynh, Tiantian Xu, Ye Wu, Kim-Han Thung, Geng Chen, Weili Lin, Dinggang Shen, Pew-Thian Yap:
Characterizing Non-Gaussian Diffusion in Heterogeneously Oriented Tissue Microenvironments. 556-563 - Xinyu Nie, Yonggang Shi:
Topographic Filtering of Tractograms as Vector Field Flows. 564-572 - Vishwesh Nath, Ilwoo Lyu, Kurt G. Schilling, Prasanna Parvathaneni, Colin B. Hansen, Yuankai Huo, Vaibhav A. Janve, Yurui Gao, Iwona Stepniewska, Adam W. Anderson, Bennett A. Landman:
Enabling Multi-shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE. 573-581 - Chuyang Ye, Yu Qin, Chenghao Liu, Yuxing Li, Xiangzhu Zeng, Zhiwen Liu:
Super-Resolved q-Space Deep Learning. 582-589 - Defu Yang, Chenggang Yan, Feiping Nie, Xiaofeng Zhu, Md Asadullah Turja, Leo Charles Peek Zsembik, Martin Styner, Guorong Wu:
Joint Identification of Network Hub Nodes by Multivariate Graph Inference. 590-598 - Fan Zhang, Nico Hoffmann, Suheyla Cetin Karayumak, Yogesh Rathi, Alexandra J. Golby, Lauren J. O'Donnell:
Deep White Matter Analysis: Fast, Consistent Tractography Segmentation Across Populations and dMRI Acquisitions. 599-608 - Dimitra Flouri, David Owen, Rosalind Aughwane, Nada Mufti, Magdalena J. Sokolska, David Atkinson, Giles S. Kendall, Alan Bainbridge, Tom Vercauteren, Anna L. David, Sébastien Ourselin, Andrew Melbourne:
Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling. 609-616 - Santiago Coelho, Jose M. Pozo, Sune Nørhøj Jespersen, Alejandro F. Frangi:
Optimal Experimental Design for Biophysical Modelling in Multidimensional Diffusion MRI. 617-625 - Itay Benou, Tammy Riklin Raviv:
DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography. 626-635 - Jean Feydy, Pierre Roussillon, Alain Trouvé, Pietro Gori:
Fast and Scalable Optimal Transport for Brain Tractograms. 636-644 - Bo Li, Wiro J. Niessen, Stefan Klein, Marius de Groot, Mohammad Arfan Ikram, Meike W. Vernooij, Esther E. Bron:
A Hybrid Deep Learning Framework for Integrated Segmentation and Registration: Evaluation on Longitudinal White Matter Tract Changes. 645-653 - Md Asadullah Turja, Leo Charles Peek Zsembik, Guorong Wu, Martin Styner:
Constructing Consistent Longitudinal Brain Networks by Group-Wise Graph Learning. 654-662
Functional Neuroimaging (fMRI)
- Zhen Zhou, Han Zhang, Li-Ming Hsu, Weili Lin, Gang Pan, Dinggang Shen:
Multi-layer Temporal Network Analysis Reveals Increasing Temporal Reachability and Spreadability in the First Two Years of Life. 665-672 - Anand A. Joshi, Haleh Akrami, Jian Li, Richard M. Leahy:
A Matched Filter Decomposition of fMRI into Resting and Task Components. 673-681 - Guoshi Li, Yujie Liu, Yanting Zheng, Ye Wu, Pew-Thian Yap, Shijun Qiu, Han Zhang, Dinggang Shen:
Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-State fMRI. 682-690 - Jiashuang Huang, Luping Zhou, Lei Wang, Daoqiang Zhang:
Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network. 691-699 - Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Pamela Ventola, James S. Duncan:
Invertible Network for Classification and Biomarker Selection for ASD. 700-708 - Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas F. Wymbs, Stewart Mostofsky, Archana Venkataraman:
Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data. 709-717 - Minjeong Kim, Amr Moussa, Peipeng Liang, Daniel Kaufer, Paul J. Laurienti, Guorong Wu:
Revealing Functional Connectivity by Learning Graph Laplacian. 718-726 - Minjeong Kim, Xiaofeng Zhu, Zi-Wen Peng, Peipeng Liang, Daniel Kaufer, Paul J. Laurienti, Guorong Wu:
Constructing Multi-scale Connectome Atlas by Learning Graph Laplacian of Common Network. 727-735 - Archit Rathore, Sourabh Palande, Jeffrey S. Anderson, Brandon A. Zielinski, P. Thomas Fletcher, Bei Wang:
Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale. 736-744 - Wei Zhang, Lin Zhao, Qing Li, Shijie Zhao, Qinglin Dong, Xi Jiang, Tuo Zhang, Tianming Liu:
Identify Hierarchical Structures from Task-Based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net. 745-753 - Tae-Eui Kam, Xuyun Wen, Bing Jin, Zhicheng Jiao, Li-Ming Hsu, Zhen Zhou, Yujie Liu, Koji Yamashita, Sheng-Che Hung, Weili Lin, Han Zhang, Dinggang Shen:
A Deep Learning Framework for Noise Component Detection from Resting-State Functional MRI. 754-762 - Wenyan Xu, Qing Li, Zhiyuan Zhu, Xia Wu:
A Novel Graph Wavelet Model for Brain Multi-scale Activational-Connectional Feature Fusion. 763-771 - Siyuan Gao, Xilin Shen, R. Todd Constable, Dustin Scheinost:
Combining Multiple Behavioral Measures and Multiple Connectomes via Multipath Canonical Correlation Analysis. 772-780 - Sukrit Gupta, Yi Hao Chan, Jagath C. Rajapakse:
Decoding Brain Functional Connectivity Implicated in AD and MCI. 781-789 - Jun Wang, Ying Zhang, Tao Zhou, Zhaohong Deng, Huifang Huang, Shitong Wang, Jun Shi, Dinggang Shen:
Interpretable Feature Learning Using Multi-output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis. 790-798 - Huzheng Yang, Xiaoxiao Li, Yifan Wu, Siyi Li, Su Lu, James S. Duncan, James C. Gee, Shi Gu:
Interpretable Multimodality Embedding of Cerebral Cortex Using Attention Graph Network for Identifying Bipolar Disorder. 799-807
Miscellaneous Neuroimaging
- Khaled Saab, Jared Dunnmon, Roger E. Goldman, Alexander Ratner, Hersh Sagreiya, Christopher Ré, Daniel L. Rubin:
Doubly Weak Supervision of Deep Learning Models for Head CT. 811-819 - Manvel Avetisian, Vladimir Kokh, Alexander Tuzhilin, Dmitry Umerenkov:
Radiologist-Level Stroke Classification on Non-contrast CT Scans with Deep U-Net. 820-828 - Yunhe Gao, Rui Huang, Ming Chen, Zhe Wang, Jincheng Deng, Yuanyuan Chen, Yiwei Yang, Jie Zhang, Chanjuan Tao, Hongsheng Li:
FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images. 829-838 - Hao Wei, Xiangyu Tang, Minqing Zhang, Qingfeng Li, Xiaodan Xing, Xiang Sean Zhou, Zhong Xue, Wenzhen Zhu, Zailiang Chen, Feng Shi:
Regression-Based Line Detection Network for Delineation of Largely Deformed Brain Midline. 839-847 - Doyoung Kwon, Jaesin Ahn, Jaeil Kim, Inchul Choi, Sungmoon Jeong, Young-Sup Lee, Jaechan Park, Minho Lee:
Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage. 848-855 - Hulin Kuang, Bijoy K. Menon, Wu Qiu:
Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network. 856-863 - Vidya M. S., Yogish Mallya, Arun Shastry, J. Vijayananda:
Recurrent Sub-volume Analysis of Head CT Scans for the Detection of Intracranial Hemorrhage. 864-872 - Runnan Chen, Yuexin Ma, Nenglun Chen, Daniel Lee, Wenping Wang:
Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting. 873-881
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