TY  - BOOK
AU  - Zhu,Dajiang
AU  - Yan,Jingwen
AU  - Huang,Heng
AU  - Shen,Li
AU  - Thompson,Paul M.
AU  - Westin,Carl-Fredrik
AU  - Pennec,Xavier
AU  - Joshi,Sarang
AU  - Nielsen,Mads
AU  - Fletcher,Tom
AU  - Durrleman,Stanley
AU  - Sommer,Stefan
ED  - MBIA (Workshop)
ED  - MFCA (Workshop)
ED  - International Conference on Medical Image Computing and Computer-Assisted Intervention
TI  - Multimodal brain image analysis and mathematical foundations of computational anatomy: 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 
T2  - Lecture notes in computer science
SN  - 9783030332266
AV  - RC386.6.D52 I58 2019eb
U1  - 616.8/04754 23
PY  - 2019///
CY  - Cham, Switzerland
PB  - Springer
KW  - Brain
KW  - Imaging
KW  - Data processing
KW  - Congresses
KW  - Image analysis
KW  - Cerveau
KW  - Imagerie
KW  - Informatique
KW  - Congrès
KW  - Analyse d'images
KW  - fast
KW  - Congress
KW  - proceedings (reports)
KW  - aat
KW  - Conference papers and proceedings
KW  - lcgft
KW  - Actes de congrès
KW  - rvmgf
N1  - International conference proceedings; Includes author index; Intro; Additional Workshop Editors; Preface; Multimodal Brain Image Analysis (MBIA); Organization; Preface; Organization; Contents; MBIA; Non-rigid Registration of White Matter Tractography Using Coherent Point Drift Algorithm; Abstract; 1 Introduction; 2 Methods; 2.1 Overview and Preprocessing; 2.2 Clustering and Central Fiber Extracting; 2.3 Non-rigid Registration; 3 Experiment and Results; 3.1 Registration Based on CPD; 3.2 Evaluation Based on Distance Between Fiber Tracts; 3.3 Evaluation Based on FA Profile Along the Fiber Tracts; 4 Conclusion; Acknowledgements; References; An Edge Enhanced SRGAN for MRI Super Resolution in Slice-Selection DirectionAbstract; 1 Introduction; 2 Method; 2.1 Two-Stage Super-Resolution Generator Network; 2.2 Discriminator Network; 2.3 Edge Enhanced Hybrid Loss Function; 3 Experiment and Result; 3.1 Dataset and Training Detail; 3.2 Results; 4 Conclusion; References; Exploring Functional Connectivity Biomarker in Autism Using Group-Wise Sparse Representation; Abstract; 1 Introduction; 2 Materials and Methods; 2.1 Overview; 2.2 Data Acquisition and Pre-Processing; 2.3 Sparse Representation Theory; 2.4 Sparse Representation of Whole-Brain FMRI Data2.5 Functional Connectivity Analysis; 3 Result; 3.1 Node Identification on Simulated Data; 3.2 Node Identification on ASD Data; 3.3 Functional Connectivity Biomarkers; 3.4 Classification Performance; 3.5 Anatomical Locations of Network Nodes; 4 Conclusion; References; Classifying Stages of Mild Cognitive Impairment via Augmented Graph Embedding; Abstract; 1 Introduction; 2 Method; 2.1 Feature Preparation; 2.2 Feature Embedding; 2.3 Feature Augmentation; 3 Experiment; 3.1 Data Description, Preprocessing and Network Reconstruction; 3.2 Experimental Settings3.3 Comparison to Other Baseline Methods; 4 Conclusion; References; Mapping the Spatio-Temporal Functional Coherence in the Resting Brain; Abstract; 1 Introduction; 2 Method; 2.1 A New Multivariate SampEn and Spatio-Temporal Coherence Mapping (STCM); 2.2 Evaluations with Numerical Simulations; 2.3 Evaluations with rsfMRI Data from Human Connectome Project (HCP); 3 Results; 3.1 Numerical Simulation Results; 3.2 Mean STIC Map and Test-Retest Stability of STIC; 3.3 Sex Effects on STIC; 3.4 Age Effects on STIC; 4 Discussion and Conclusion; Acknowledgement; References; Species-Preserved Structural Connections Revealed by Sparse Tensor CCAAbstract; 1 Introduction; 2 Materials and Methods; 2.1 Datasets; 2.2 Data Preprocessing; 2.3 Structural Connective Connectome Construction; 2.4 Sparse Tensor Canonical Correlation Analysis (STCCA); 3 Results; 3.1 Cross-Validation; 3.2 DTI Tracts Comparison Between Human and Macaque; 4 Conclusion; References; Identification of Abnormal Cortical 3-Hinge Folding Patterns on Autism Spectral Brains; Abstract; 1 Introduction; 2 Materials and Methods; 2.1 Datasets and Preprocessing; 2.2 3-Hinges Detection
N2  - This book constitutes the refereed joint proceedings of the 4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 16 full papers presented at MBAI 2019 and the 7 full papers presented at MFCA 2019 were carefully reviewed and selected. The MBAI papers intend to move forward the state of the art in multimodal brain image analysis, in terms of analysis methodologies, algorithms, software systems, validation approaches, benchmark datasets, neuroscience, and clinical applications. The MFCA papers are devoted to statistical and geometrical methods for modeling the variability of biological shapes. The goal is to foster the interactions between the mathematical community around shapes and the MICCAI community around computational anatomy applications
UR  - https://link.springer.com/10.1007/978-3-030-33226-6
ER  -