Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Springer International Publishing (Verlag)
978-3-030-32225-0 (ISBN)
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.
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.-
| Erscheinungsdatum | 15.10.2019 |
|---|---|
| Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
| Zusatzinfo | XXXVIII, 860 p. 476 illus., 308 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1353 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Technik | |
| Schlagworte | Applications • Artificial Intelligence • Computed tomography • Computer Aided Diagnosis • computer assisted interventions • Computer Science • conference proceedings • Image Processing • image reconstruction • Image Segmentation • Imaging Systems • Informatics • Learning Algorithms • machine learning • Medical Images • Neural networks • neuroimage reconstruction • neuroimage segmentation • Optical imaging • Research • segmentation methods • Support Vector Machines • SVM • x-ray imaging |
| ISBN-10 | 3-030-32225-4 / 3030322254 |
| ISBN-13 | 978-3-030-32225-0 / 9783030322250 |
| Zustand | Neuware |
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