Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 : 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV / edited by Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz

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Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12264 | Springer eBook CollectionPublisher: Cham : Springer International Publishing, 2020Publisher: Cham : Imprint: Springer, 2020Edition: 1st ed. 2020Description: 1 Online-Ressource(XXXVII, 831 p. 22 illus.)ISBN:

9783030597191

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Additional physical formats: 9783030597184 | 9783030597207 | Erscheint auch als: 9783030597184 Druck-Ausgabe | Erscheint auch als: 9783030597207 Druck-AusgabeDDC classification:

006.6 23
006.37 23

DOI: DOI: 10.1007/978-3-030-59719-1Online resources:

Summary: Segmentation -- Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression -- DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision -- KISEG: A Three-Stage Segmentation Framework for Multi-level Acceleration of Chest CT Scans from COVID-19 Patients -- CircleNet: Anchor-free Glomerulus Detection with Circle Representation -- Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies -- Diagnostic Assessment of Deep Learning Algorithms for Detection and Segmentation of Lesion in Mammographic images -- Efficient and Phase-aware Video Super-resolution for Cardiac MRI -- ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease -- Deep Generative Model-based Quality Control for Cardiac MRI Segmentation -- DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation -- Learning Directional Feature Maps for Cardiac MRI Segmentation -- Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention -- XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms -- TexNet: Texture Loss Based Network for Gastric Antrum Segmentation in Ultrasound -- Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets -- Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling -- Pay More Attention to Discontinuity for Medical Image Segmentation -- Learning 3D Features with 2D CNNs via Surface Projection for CT Volume Segmentation -- Deep Class-specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation -- Memory-efficient Automatic Kidney and Tumor Segmentation Based on Non-local Context Guided 3D U-Net -- Deep Small Bowel Segmentation with Cylindrical Topological Constraints -- Learning Sample-adaptive Intensity Lookup Table for Brain Tumor Segmentation -- Superpixel-Guided Label Softening for Medical Image Segmentation -- Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation -- Robust Medical Image Segmentation from Non-expert Annotations with Tri-network -- Robust Fusion of Probability Maps -- Calibrated Surrogate Maximization of Dice -- Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices -- Widening the focus: biomedical image segmentation challenges and the underestimated role of patch sampling and inference strategies -- Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data -- Unsupervised Learning for CT Image Segmentation via Adversarial Redrawing -- Deep Active Contour Network for Medical Image Segmentation -- Learning Crisp Edge Detector Using Logical Refinement Network -- Defending Deep Learning-based Biomedical Image Segmentation from Adversarial Attacks: A Low-cost Frequency Refinement Approach -- CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation -- KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations -- LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation -- INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs -- SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos -- Orchestrating Medical Image Compression and Remote Segmentation Networks -- Bounding Maps for Universal Lesion Detection -- Multimodal Priors Guided Segmentation of Liver Lesions in MRI Using Mutual Information Based Graph Co-Attention Networks -- Mt-UcGAN: Multi-task uncertainty-constrained GAN for joint segmentation, quantification and uncertainty estimation of renal tumors on CT -- Weakly Supervised Deep Learning for Breast Cancer Segmentation with Coarse Annotations -- Multi-phase and Multi-level Selective Feature Fusion for Automated Pancreas Segmentation from CT Images -- Asymmetrical Multi-Task Attention U-Net for the Segmentation of Prostate Bed in CT Image -- Learning High-Resolution and Efficient Non-local Features for Brain Glioma Segmentation in MR Images -- Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans -- Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks -- E2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans -- Universal loss reweighting to balance lesion size inequality in 3D medical image segmentation -- Brain tumor segmentation with missing modalities via latent multi-source correlation representation -- Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices -- Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI -- AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes -- One Click Lesion RECIST Measurement and Segmentation on CT Scans -- Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI -- Deep Attentive Panoptic Model for Prostate Cancer Detection Using Biparametric MRI Scans -- Shape Models and Landmark Detection -- Graph Reasoning and Shape Constraints for Cardiac Segmentation in Congenital Heart Defect -- Nonlinear Regression on Manifolds for Shape Analysis using Intrinsic Bézier Splines -- Self-Supervised Discovery of Anatomical Shape Landmarks -- Shape Mask Generator: Learning to Refine Shape Priors for Segmenting Overlapping Cervical Cytoplasms -- Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes -- Deep Learning Assisted Automatic Intra-operative 3D Aortic Deformation Reconstruction -- Landmarks Detection with Anatomical Constraints for Total Hip Arthroplasty Preoperative Measurements -- Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle -- Miss the point: Targeted adversarial attack on multiple landmark detection -- Automatic Tooth Segmentation and Dense Correspondence of 3D Dental Model -- Move over there: One-click deformation correction for image fusion during endovascular aortic repair -- Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model -- Deformation Aware Augmented Reality for Craniotomy using 3D/2D Non-rigid Registration of Cortical Vessels -- Skip-StyleGAN: Skip-connected Generative Adversarial Networks for Generating 3D Rendered Image of Hand Bone Complex -- Dynamic multi-object Gaussian process models -- A kernelized multi-level localization method for flexible shape modeling with few training data -- Unsupervised Learning and Statistical Shape Modeling of the Morphometry and Hemodynamics of Coarctation of the Aorta -- Convolutional Bayesian Models for Anatomical Landmarking on Multi-Dimensional Shapes -- SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation -- Multi-Task Dynamic Transformer Network for Concurrent Bone Segmentation and Large-Scale Landmark Localization with Dental CBCT -- Automatic Localization of Landmarks in Craniomaxillofacial CBCT Images using a Local Attention-based Graph Convolution Network.Summary: The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. 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: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography.PPN: PPN: 1737522039Package identifier: Produktsigel: ZDB-2-LNC | ZDB-2-SCS | ZDB-2-SXCS | ZDB-2-SEB

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