Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part VI / [electronic resource] :
edited by Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz.
- 1st ed. 2020.
- XXXVII, 819 p. 33 illus., 1 illus. in color. online resource.
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12266 3004-9954 ; .
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12266 .
Angiography and Vessel Analysis -- Lightweight Double Attention-fused Networks for Intraoperative Stent Segmentation -- TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling -- Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction -- Branch-aware Double DQN for Centerline Extraction in Coronary CT Angiography -- Automatic CAD-RADS Scoring from CCTA Scans using Deep Learning -- Higher-Order Flux with Spherical Harmonics Transform for Vascular Analysis -- Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network -- Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement -- Time matters: Handling spatio-temporal perfusion information for automated TICI scoring -- ID-Fit: Intra-saccular Device adjustment for personalized cerebral aneurysm treatment -- JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation -- Classification of Retinal Vessels into Artery-Vein in OCT Angiography Guided by Fundus Images -- Vascular surface segmentation for intracranial aneurysm isolation and quantification -- Breast Imaging -- Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities -- 2D X-ray mammography and 3D breast MRI registration -- A Second-order Subregion Pooling Network for Breast Ultrasound Lesion Segmentation -- Multi-Scale Gradational-Order Fusion Framework for Breast lesions Classification Using Ultrasound images -- Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network -- Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound -- MommiNet: Mammographic Multi-View Mass Identification Networks -- Multi-Site Evaluation of a Study-Level Classifier for Mammography using Deep Learning -- The case of missed cancers: Applying AI as a radiologist's safety net -- Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models -- Multi-task learning for detection and classification of cancer in screening mammography -- Colonoscopy -- Adaptive Context Selection for Polyp Segmentation -- PraNet: Parallel Reverse Attention Network for Polyp Segmentation -- Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy -- PolypSeg: an Efficient Context-aware Network for Polyp Segmentation from Colonoscopy Videos -- Endoscopic polyp segmentation using a hybrid 2D/3D CNN -- Dermatology -- A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images -- Fairness of Classifiers Across Skin Tones in Dermatology -- Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification -- Clinical-Inspired Network for Skin Lesion Recognition -- Multi-class Skin Lesion Segmentation for Cutaneous T-cell Lymphomas on High-Resolution Clinical Images -- Fetal Imaging -- Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets -- Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect -- Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency -- Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy -- Automatic angle of progress measurement of intrapartum transperineal ultrasound image with deep learning -- Joint Image Quality Assessment and Brain Extraction of Fetal MRI using Deep Learning -- Heart and Lung Imaging -- Accelerated 4D Respiratory Motion-resolved Cardiac MRI with a Model-based Variational Network -- Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation -- ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation -- A Bottom-up Approach for Real-time Mitral Valve Annulus Modeling on 3D Echo Images -- A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking andSegmentation in 4D Echocardiography -- Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets -- Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction -- Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study -- Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules -- Multi-stream Progressive Up-sampling Network for Dense CT Image Reconstruction -- Abnormality Detection in Chest X-ray Images Using Uncertainty Prediction Autoencoders -- Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification -- CPM-Net: A 3D Center-Points Matching Network for Pulmonary Nodule Detection in CT Scans -- Interpretable Identification of Interstitial Lung Diseases (ILD) Associated Findings from CT -- Learning with Sure Data for Nodule-Level Lung Cancer Prediction -- Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation -- Class-Aware Multi-Window Adversarial Lung Nodule Synthesis Conditioned on Semantic Features -- Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation -- Deep Active Learning for Effective Pulmonary Nodule Detection -- Musculoskeletal Imaging -- Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity -- Improve bone age assessment by learning from anatomical local regions -- An Analysis by Synthesis Method that Allows Accurate Spatial Modeling of Thickness of Cortical Bone from Clinical QCT -- Segmentation of Paraspinal Muscles at Varied Lumbar Spinal Levels by Explicit Saliency-Aware Learning -- Manifold Ordinal-Mixup for Ordered Classes inTW3-based Bone Age Assessment -- Contour-based Bone Axis Detection for X-Ray Guided Surgery on the Knee -- Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks -- Discriminative dictionary-embedded network for comprehensivevertebrae tumor diagnosis -- Multi-vertebrae segmentation from arbitrary spine MR images under global view -- A Convolutional Approach to Vertebrae Identification in Whole Spine MRI -- Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification -- Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection -- 3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT -- SIMBA: Specific Identity Markers for Bone Age Assessment -- Doctor Imitator: A Graph-based Bone Age Assessment Framework Using Hand Radiographs -- Inferring the 3D Standing Spine Posture from 2D Radiographs -- Generative Modelling of 3D in-silico Spongiosa with Controllable Micro-Structural Parameters -- GAN-based Realistic Bone Ultrasound Image and Label Synthesis for Improved Segmentation -- Robust Bone Shadow Segmentation from 2D Ultrasound Through Task Decomposition.
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.
9783030597252
10.1007/978-3-030-59725-2 doi
Computer vision.
Artificial intelligence.
Social sciences--Data processing.
Education--Data processing.
Pattern recognition systems.
Bioinformatics.
Computer Vision.
Artificial Intelligence.
Computer Application in Social and Behavioral Sciences.
Computers and Education.
Automated Pattern Recognition.
Computational and Systems Biology.
TA1634
006.37
Angiography and Vessel Analysis -- Lightweight Double Attention-fused Networks for Intraoperative Stent Segmentation -- TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling -- Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction -- Branch-aware Double DQN for Centerline Extraction in Coronary CT Angiography -- Automatic CAD-RADS Scoring from CCTA Scans using Deep Learning -- Higher-Order Flux with Spherical Harmonics Transform for Vascular Analysis -- Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network -- Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement -- Time matters: Handling spatio-temporal perfusion information for automated TICI scoring -- ID-Fit: Intra-saccular Device adjustment for personalized cerebral aneurysm treatment -- JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation -- Classification of Retinal Vessels into Artery-Vein in OCT Angiography Guided by Fundus Images -- Vascular surface segmentation for intracranial aneurysm isolation and quantification -- Breast Imaging -- Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities -- 2D X-ray mammography and 3D breast MRI registration -- A Second-order Subregion Pooling Network for Breast Ultrasound Lesion Segmentation -- Multi-Scale Gradational-Order Fusion Framework for Breast lesions Classification Using Ultrasound images -- Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network -- Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound -- MommiNet: Mammographic Multi-View Mass Identification Networks -- Multi-Site Evaluation of a Study-Level Classifier for Mammography using Deep Learning -- The case of missed cancers: Applying AI as a radiologist's safety net -- Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models -- Multi-task learning for detection and classification of cancer in screening mammography -- Colonoscopy -- Adaptive Context Selection for Polyp Segmentation -- PraNet: Parallel Reverse Attention Network for Polyp Segmentation -- Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy -- PolypSeg: an Efficient Context-aware Network for Polyp Segmentation from Colonoscopy Videos -- Endoscopic polyp segmentation using a hybrid 2D/3D CNN -- Dermatology -- A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images -- Fairness of Classifiers Across Skin Tones in Dermatology -- Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification -- Clinical-Inspired Network for Skin Lesion Recognition -- Multi-class Skin Lesion Segmentation for Cutaneous T-cell Lymphomas on High-Resolution Clinical Images -- Fetal Imaging -- Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets -- Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect -- Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency -- Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy -- Automatic angle of progress measurement of intrapartum transperineal ultrasound image with deep learning -- Joint Image Quality Assessment and Brain Extraction of Fetal MRI using Deep Learning -- Heart and Lung Imaging -- Accelerated 4D Respiratory Motion-resolved Cardiac MRI with a Model-based Variational Network -- Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation -- ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation -- A Bottom-up Approach for Real-time Mitral Valve Annulus Modeling on 3D Echo Images -- A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking andSegmentation in 4D Echocardiography -- Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets -- Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction -- Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study -- Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules -- Multi-stream Progressive Up-sampling Network for Dense CT Image Reconstruction -- Abnormality Detection in Chest X-ray Images Using Uncertainty Prediction Autoencoders -- Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification -- CPM-Net: A 3D Center-Points Matching Network for Pulmonary Nodule Detection in CT Scans -- Interpretable Identification of Interstitial Lung Diseases (ILD) Associated Findings from CT -- Learning with Sure Data for Nodule-Level Lung Cancer Prediction -- Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation -- Class-Aware Multi-Window Adversarial Lung Nodule Synthesis Conditioned on Semantic Features -- Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation -- Deep Active Learning for Effective Pulmonary Nodule Detection -- Musculoskeletal Imaging -- Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity -- Improve bone age assessment by learning from anatomical local regions -- An Analysis by Synthesis Method that Allows Accurate Spatial Modeling of Thickness of Cortical Bone from Clinical QCT -- Segmentation of Paraspinal Muscles at Varied Lumbar Spinal Levels by Explicit Saliency-Aware Learning -- Manifold Ordinal-Mixup for Ordered Classes inTW3-based Bone Age Assessment -- Contour-based Bone Axis Detection for X-Ray Guided Surgery on the Knee -- Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks -- Discriminative dictionary-embedded network for comprehensivevertebrae tumor diagnosis -- Multi-vertebrae segmentation from arbitrary spine MR images under global view -- A Convolutional Approach to Vertebrae Identification in Whole Spine MRI -- Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification -- Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection -- 3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT -- SIMBA: Specific Identity Markers for Bone Age Assessment -- Doctor Imitator: A Graph-based Bone Age Assessment Framework Using Hand Radiographs -- Inferring the 3D Standing Spine Posture from 2D Radiographs -- Generative Modelling of 3D in-silico Spongiosa with Controllable Micro-Structural Parameters -- GAN-based Realistic Bone Ultrasound Image and Label Synthesis for Improved Segmentation -- Robust Bone Shadow Segmentation from 2D Ultrasound Through Task Decomposition.
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.
9783030597252
10.1007/978-3-030-59725-2 doi
Computer vision.
Artificial intelligence.
Social sciences--Data processing.
Education--Data processing.
Pattern recognition systems.
Bioinformatics.
Computer Vision.
Artificial Intelligence.
Computer Application in Social and Behavioral Sciences.
Computers and Education.
Automated Pattern Recognition.
Computational and Systems Biology.
TA1634
006.37