Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, proceedings
著者
書誌事項
Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, proceedings
(Lecture notes in computer science, 11766 . LNCS sublibrary ; SL 6 . Image processing,
Springer, c2019
- pt. 3
- タイトル別名
-
MICCAI 2019
大学図書館所蔵 全1件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Other editors: Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Includes bibliographical references and author index
内容説明・目次
内容説明
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019.
The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: optical imaging; endoscopy; microscopy.
Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression.
Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging.
Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis.
Part V: computer assisted interventions; MIC meets CAI.
Part VI: computed tomography; X-ray imaging.
目次
Neuroimage Reconstruction and Synthesis.- Isotropic MRI Super-Resolution Reconstruction with Multi-Scale Gradient Field Prior.- A Two-Stage Multi-Loss Super-Resolution Network For Arterial Spin Labeling Magnetic Resonance Imaging.- Model Learning: Primal Dual Networks for Fast MR imaging.- Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging.- Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework.- Deep Learning Based Framework for Direct Reconstruction of PET Images.- Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction.- Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans using Sparse Fidelity Loss and Adversarial Regularization.- Single Image Based Reconstruction of High Field-like MR Images.- Deep Neural Network for QSM Background Field Removal.- RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting.- RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting.- GANReDL: Medical Image enhancement using a generative adversarial network with real-order derivative induced loss functions.- Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks.- Semi-Supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control.- Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-Modal Neuroimages.- Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map.- CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading.- Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression.- Neuroimage Segmentation.- Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation.- 3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI.- Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants.- VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation.- Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning.- Scalable Neural Architecture Search for 3D Medical Image Segmentation.- Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images.- High Resolution Medical Image Segmentation using Data-swapping Method.- X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies.- Multi-View Semi-supervised 3D Whole Brain Segmentation with a Self-Ensemble Network.- CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke.- Brain Segmentation from k-space with End-to-end Recurrent Attention Network.- Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images.- CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion.- A Joint 3D+2D Fully Convolutional Framework for Subcortical Segmentation.- U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets.- Generative adversarial network for segmentation of motion affected neonatal brain MRI.- Interactive deep editing framework for medical image segmentation.- Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices.- Improving Multi-Atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation.- Unsupervised deep learning for Bayesian brain MRI segmentation.- Online atlasing using an iterative centroid.- ARS-Net: Adaptively Rectified Supervision Network for Automated 3D Ultrasound Image Segmentation.- Complete Fetal Head Compounding from Multi-View 3D Ultrasound.- SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation.- Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation.- RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation.- Deep Cascaded Attention Networks for Multi-task Brain Tumor Segmentation.- Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation.- 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation.- Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion.- Multi-task Attention-based Semi-supervised Learning for Medical Image Segmentation.- AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation.- Automated Parcellation of the Cortex using Structural Connectome Harmonics.- Hierarchical parcellation of the cerebellum.- Intrinsic Patch-based Cortical Anatomical Parcellation using Graph Convolutional Neural Network on Surface Manifold.- Cortical Surface Parcellation using Spherical Convolutional Neural Networks.- A Soft STAPLE Algorithm Combined with Anatomical Knowledge.- Diffusion Weighted Magnetic Resonance Imaging.- Multi-Stage Image Quality Assessment of Diffusion MRI via Semi-Supervised Nonlocal Residual Networks.- Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks.- Surface-based Tracking of U-fibers in the Superficial White Matter.- Probing Brain Micro-Architecture by Orientation Distribution Invariant Identification of Diffusion Compartments.- Characterizing Non-Gaussian Diffusion in Heterogeneously Oriented Tissue Microenvironments.- Topographic Filtering of Tractograms as Vector Field Flows.- Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE.- Super-Resolved q-Space Deep Learning.- Joint Identification of Network Hub Nodes by Multivariate Graph Inference.- Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions.- Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling.- Optimal experimental design for biophysical modelling in multidimensional diffusion MRI.- DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography .- Fast and Scalable Optimal Transport for Brain Tractograms.- A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes.- Constructing Consistent Longitudinal Brain Networks by Group-wise Graph Learning.- Functional Neuroimaging (fMRI).- Multi-layer temporal network analysis reveals increasing temporal reachability and spreadability in the first two years of life.- A matched filter decomposition of fMRI into resting and task components.- Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-state fMRI.- Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network.- Invertible Network for Classification and Biomarker Selection for ASD.- Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data.- Revealing Functional Connectivity by Learning Graph Laplacian.- Constructing Multi-Scale Connectome Atlas by Learning Common Topology of Brain Networks.- Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale.- Identify Hierarchical Structures from Task-based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net.- A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI.- A Novel Graph Wavelet Model for Brain Multi-Scale Functional-structural Feature Fusion.- Combining Multiple Behavioral Measures and Multiple Connectomes via Multiway Canonical Correlation Analysis.- Decoding brain functional connectivity implicated in AD and MCI.- Interpretable Feature Learning Using Multi-Output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis.- Interpretable Multimodality Embedding Of Cerebral Cortex Using Attention Graph Network For Identifying Bipolar Disorder.- Miscellaneous Neuroimaging.- Doubly Weak Supervision of Deep Learning Models for Head CT.- Detecting Acute Strokes from Non-Contrast CT Scan Data Using Deep Convolutional Neural Networks.- FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images.- Regression-based Line Detection Network for Delineation of Largely Deformed Brain Midline.- Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage.- Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network.- Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage.- Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting.
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