Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Springer International Publishing (Verlag)
978-3-030-32244-1 (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.
Image Segmentation.- Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation.- Comparative Evaluation of Hand-Engineered and Deep-Learned Features for Neonatal Hip Bone Segmentation in Ultrasound.- Unsupervised Quality Control of Image Segmentation based on Bayesian Learning.- One Network To Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation.- 'Project & Excite' Modules for Segmentation of Volumetric Medical Scans.- Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation.- Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation.- Extreme Points Derived Confidence Map as a Cue For Class-Agnostic Segmentation Using Deep Neural Network.- Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation.- Instance Segmentation from Volumetric Biomedical Images without Voxel-Wise Labeling.- Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice.- Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation.- HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images.- PHiSeg: Capturing Uncertainty in Medical Image Segmentation.- Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data.- Supervised Uncertainty Quantification for Segmentation with Multiple Annotations.- 3D Tiled Convolution for Effective Segmentation of Volumetric Medical Images.- Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation.- Statistical intensity- and shape-modeling to automate cerebrovascular segmentation from TOF-MRA data.- Segmentation of Vessels in Ultra High Frequency Ultrasound Sequences using Contextual Memory.- Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion.- Mixed-Supervised Dual-Network for Medical Image Segmentation.- Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks.- Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation.- Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images.- Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation.- Radiomics-guided GAN for Segmentation of Liver Tumor without Contrast Agents.- Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks.- Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation.- Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss.- Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation.- Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation.- 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation.- Impact of Adversarial Examples on Deep Learning Segmentation Models.- Multi-Resolution Path CNN with Deep Supervision for Intervertebral Disc Localization and Segmentation.- Automatic paraspinal muscle segmentation in patients with lumbar pathology using deep convolutional neural network.- Constrained Domain Adaptation for Segmentation.- Image Registration.-Image-and-Spatial Transformer Networks for Structure-Guided Image Registration.- Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration.- A deep learning approach to MR-less spatial normalization for tau PET images.- TopAwaRe: Topology-Aware Registration.- Multimodal Data Registration for Brain Structural Association Networks.- Dual-Stream Pyramid Registration Network.- A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration.- Conditional Segmentation in Lieu of Image Registration.- On the applicability of registration uncer
| Erscheinungsdatum | 20.10.2019 |
|---|---|
| Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
| Zusatzinfo | XXXVI, 874 p. 347 illus., 312 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1371 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 | 3-030-32244-0 / 3030322440 |
| ISBN-13 | 978-3-030-32244-1 / 9783030322441 |
| Zustand | Neuware |
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