Kooperativer Bibliotheksverbund

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Format: XXXVI, 626 p. 241 illus., 212 illus. in color. , online resource.

Edition: 1st ed. 2021.

ISBN: 9783030872311

Series Statement: Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12906

Content: The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging - others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.

Note: Image Reconstruction -- Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images -- Over-and-Under Complete Convolutional RNN for MRI Reconstruction -- TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation -- Synthesizing Multi-Tracer PET Images for Alzheimer's Disease Patients using a 3D Unified Anatomy-aware Cyclic Adversarial Network -- Generalised Super Resolution for Quantitative MRI Using Self-Supervised Mixture of Experts -- TransCT: Dual-path Transformer for Low Dose Computed Tomography -- IREM: High-Resolution Magnetic Resonance Image Reconstruction via Implicit Neural Representation -- DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images -- Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation -- Fast Magnetic Resonance Imaging on Regions of Interest: From Sensing to Reconstruction -- InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction -- Depth Estimation for Colonoscopy Images with Self-supervised Learning from Videos -- Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy -- Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network -- Generator Versus Segmentor: Pseudo-healthy Synthesis -- Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting -- Estimation of High Frame Rate Digital Subtraction Angiography Sequences at Low Radiation Dose -- RLP-Net: Recursive Light Propagation Network for 3-D Virtual Refocusing -- Noise Mapping and Removal in Complex-Valued Multi-Channel MRI via Optimal Shrinkage of Singular Values -- Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction -- Universal Undersampled MRI Reconstruction -- A Neural Framework for Multi-Variable Lesion Quantification Through B-mode Style Transfer -- Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction -- Dual-Domain Adaptive-Scaling Non-Local Network for CT Metal Artifact Reduction -- Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution -- Adaptive Squeeze-and-Shrink Image Denoising for Improving Deep Detection of Cerebral Microbleeds -- 3D Transformer-GAN for High-quality PET Reconstruction -- Learnable Multi-scale Fourier Interpolation for Sparse View CT Image Reconstruction -- U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction -- Task Transformer Network for Joint MRI Reconstruction and Super-Resolution -- Conditional GAN with an Attention-based Generator and a 3D Discriminator for 3D Medical Image Generation -- Multimodal MRI Acceleration via Deep Cascading Networks with Peer-layer-wise Dense Connections -- Rician noise estimation for 3D Magnetic Resonance Images based on Benford's Law -- Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization -- Label-Free Physics-Informed Image Sequence Reconstruction with Disentangled Spatial-Temporal Modeling -- High-Resolution Hierarchical Adversarial Learning for OCT Speckle Noise Reduction -- Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework -- Acceleration by deep-learnt sharing of superfluous information in multi-contrast MRI -- Sequential Lung Nodule Synthesis using Attribute-guided Generative Adversarial Networks -- A Data-driven Approach for High Frame Rate Synthetic Transmit Aperture Ultrasound Imaging -- Interpretable deep learning for multimodal super-resolution of medical images -- MRI Super-Resolution Through Generative Degradation Learning -- Task-Oriented Low-Dose CT Image Denoising -- Revisiting contour-driven and knowledge-based deformable models: application to 2D-3D proximal femur reconstruction from X-ray images -- Memory-efficient Learning for High-dimensional MRI Reconstruction -- SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation -- Clinical Applications - Cardiac -- Distortion Energy for Deep Learning-based Volumetric Finite Element Mesh Generation for Aortic Valves -- Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation -- EchoCP: An Echocardiography Dataset in Contrast Transthoracic Echocardiography for Patent Foramen Ovale Diagnosis -- Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries -- Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views -- Phase-independent Latent Representation for Cardiac Shape Analysis -- Cardiac Transmembrane Potential Imaging with GCN Based Iterative Soft Threshold Network -- AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs -- TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline -- Clinical Applications - Vascular -- Deep Open Snake Tracker for Vessel Tracing -- MASC-Units: Training Oriented Filters for Segmenting Curvilinear Structures -- Vessel Width Estimation via Convolutional Regression -- Renal Cell Carcinoma Classification from Vascular Morphology.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783030872304

Additional Edition: Printed edition: ISBN 9783030872328

Language: English

DOI: 10.1007/978-3-030-87231-1

URL: https://doi.org/10.1007/978-3-030-87231-1