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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI de Dinggang Shen

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI: Lecture Notes in Computer Science, cartea 11769

Editat de Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
en Limba Engleză Paperback – 13 oct 2019
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.

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Specificații

ISBN-13: 9783030322250
ISBN-10: 3030322254
Pagini: 860
Ilustrații: XXXVIII, 860 p. 476 illus., 308 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 1.23 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Cuprins

Computed Tomography.- Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma.- MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection.- Spatial-Frequency Non-Local Convolutional LSTM Network for pRCC classification.- BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization.- Abdominal Adipose Tissue Segmentation in MRI with Double Loss Function Collaborative Learning.- Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks.- Generating Pareto optimal dose distributions for radiation therapy treatment planning.- PAN: Projective Adversarial Network for Medical Image Segmentation.- Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction.- Multi-Class Gradient Harmonized Dice Loss with Application to Knee MR Image Segmentation.- LSRC: A Long-Short Range Context-Fusing Framework for Automatic 3D Vertebra Localization.- Contextual Deep Regression Network for Volume Estimation in Orbital CT.- Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images.- Deep Learning based Metal Artifacts Reduction in post-operative Cochlear Implant CT Imaging.- ImHistNet: Learnable Image Histogram Based DNN with Application to Noninvasive Determination of Carcinoma Grades in CT Scans.- DPA-DenseBiasNet: Semi-supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and Deep Priori Anatomy.- Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior.- Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network.- Unsupervised Deformable Image Registration Using Cycle-Consistent CNN.- Volumetric Attention for 3D Medical Image Segmentation and Detection.- Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention.- MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation.- Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction.- AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks.- Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation.- Bronchus Segmentation and Classification by Neural Networks and Linear Programming.- Unsupervised Segmentation of Micro-CT Images of Lung Cancer Specimen Using Deep Generative Models.- Normal appearance autoencoder for lung cancer detection and segmentation.- mlVIRNET: Multilevel Variational Image Registration Network.- NoduleNet: Decoupled False Positive Reduction for Pulmonary Nodule Detection and Segmentation.- Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition.- Targeting Precision with Data Augmented Samples in Deep Learning.- Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images.- Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster.- Deep Variational Networks with Exponential Weighting for Learning Computed Tomography.- R2-Net: Recurrent and Recursive Network for Sparse-view CT Artifacts Removal.- Stereo-Correlation and Noise-Distribution Aware ResVoxGAN for Dense Slices Reconstruction and Noise Reduction in Thick Low-Dose CT.- Harnessing 2D Networks and 3D Features for Automated Pancreas Segmentation from Volumetric CT Images.- Tubular Structure Segmentation Using Spatial Fully Connected Network With Radial Distance Loss for 3D Medical Images.- Bronchial Cartilage Assessment with Model-Based GAN Regressor.- Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy.- Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis.- AutomaticallyLocalizing a Large Set of Spatially Correlated Key Points: A Case Study in Spine Imaging.- Permutohedral Attention Module for Efficient Non-Local Neural Networks.- Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels.- X-ray Imaging.- PRSNet: Part Relation and Selection Network for Bone Age Assessment.- Mask Embedding for Realistic High-resolution Medical Image Synthesis.- TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays.- Misshapen Pelvis Landmark Detection by Spatial Local Correlation Mining for Diagnosing Developmental Dysplasia of the Hip.- Adversarial Policy Gradient for Deep Learning Image Augmentation.- Weakly Supervised ROI Mining Toward Universal Fracture Detection in Pelvic X-ray.- Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography.- From Unilateral to Bilateral Learning: Detecting MammogramMass with Contrasted Bilateral Network.- Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis.- Uncertainty measurements for the reliable classification of mammograms.- GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision.- 3DFPN-HS2: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection.- Automated detection and type classification of central venous catheters in chest X-rays.- A Comprehensive Framework for Accurate Classification of Pulmonary Nodules.- Hand Pose Estimation for Pediatric Bone Age Assessment.- An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms.- Learning-based X-ray Image Denoising utilizing Model-based Image Simulations.- LVC-Net: Medical image segmentation with noisy label based on Local Visual Cues.- Unsupervised Cone-Beam Computed Tomography (CBCT) segmentation based on adversarial learning domain adaptation.- Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation.- Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders.- Simultaneous Lung Field Detection and Segmentation for Pediatric ChestRadiographs.- Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumor, Lymph Nodes, and Organs At Risk.- Weakly Supervised Segmentation Framework with Uncertainty: A Study on Pneumothorax Segmentation in Chest X-ray.- Multi-task Localization and Segmentation for X-ray Guided Planning in Knee Surgery.- Towards fully automatic X-ray to CT registration.- Adaptive image-feature learning for disease classification using inductive graph networks.- How to learn from unlabeled volume data: Self-Supervised 3D Context Feature Learning.- Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis.- Extract Bone Parts without Human Prior: End-to-end Convolutional Neural Network for Pediatric Bone Age Assessment.- Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment.- Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays.- Medical-based Deep Curriculum Learning for Improved Fracture Classification.- Realistic Breast Mass Generation through BIRADS Category.- Learning from Longitudinal Mammography Studies.- Automated Radiology Report Generation via Multi-view Image Fusion and Medical Concept Enrichment.- Multi-label Thoracic Disease Image Classification with Cross-attention Networks.- InfoMask: Masked Variational Latent Representation to Localize Chest Disease.- Longitudinal Change Detection on Chest X-rays using Geometric Correlation Maps.- Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation.- Semi-Supervised Learning by Disentangling and Self-Ensembling over Stochastic Latent Space.- An Automated Cobb Angle Estimation Method Using Convolutional Neural Network with Area Limitation.- Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data.- Learning Interpretable Features via Adversarially Robust Optimization.- Synthesize Mammogram from Digital Breast Tomosynthesis with Gradient Guided cGANs.- Semi-supervised Medical Image Segmentation via Learning Consistency under Transformations.- Improved Inference via Deep Input Transfer.- Neural Architecture Search for Adversarial Medical Image Segmentation.- MeshSNet: Deep Multi-Scale Mesh Feature Learning for End-to-End Tooth Labeling on 3D Dental Surfaces.- Improving Robustness of Medical Image Diagnosis with Denoising Convolutional Neural Networks.