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[null,null,"Titolo",null,"Medical image understanding and analysis : 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12-14, 2021, proceedings / / edited by Bartłomiej W. Papież [and four others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-80432-1"]
[null,null,"Descrizione fisica",null,"1 online resource (566 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.12722"]
[null,null,"Disciplina",null,"616.0754"]
[null,null,"Soggetti",null,"Diagnostic imaging"]
[{"$nulls":4},"Diagnostic imaging - Data processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Biomarker Detection -- Exploring the Correlation Between Deep Learned and Clinical Features in Melanoma Detection -- 1 Introduction -- 2 Dataset and Methodology -- 2.1 Dataset: Description and Pre-processing -- 2.2 Deep Architectures -- 2.3 ABCD Clinical Features and Classification -- 3 Experiments and Results -- 3.1 Quantitative Results -- 3.2 Alignment Between ABCD Features and Deep Learned Features -- 3.3 Qualitative Results -- 4 Conclusion -- References -- An Efficient One-Stage Detector for Real-Time Surgical Tools Detection in Robot-Assisted Surgery -- 1 Introduction -- 2 Methodology -- 2.1 Network Architecture -- 2.2 Loss Function for Learning -- 3 Experiment and Results -- 3.1 Dataset -- 3.2 Experiment Settings -- 3.3 Results -- 4 Conclusion -- References -- A Comparison of Computer Vision Methods for the Combined Detection of Glaucoma, Diabetic Retinopathy and Cataracts -- 1 Introduction -- 2 Research Problem and Environment -- 3 Literature Review -- 4 Experiment Setup -- 5 Model -- 5.1 Image Acquisition -- 5.2 The Composition of the Trained Models and Classifiers -- 5.3 Pre-processing -- 5.4 Feature Extraction and Classification -- 5.5 Displaying the Results -- 6 Results -- 7 Result Analysis -- 8 Conclusion -- References -- Prostate Cancer Detection Using Image-Based Features in Dynamic Contrast Enhanced MRI -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Feature Extraction -- 2.4 Classification -- 3 "]
[{"$nulls":4},"Results -- 3.1 Comparison of Image-Based, Pharmacokinetic and Perfusion-Related Features -- 3.2 Aggregation of All Features -- 4 Discussion -- 5 Conclusions -- References -- Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR Images -- 1 Introduction -- 2 Methods -- 2.1 Problem Definition."]
[{"$nulls":4},"2.2 Overall Training Loss Function -- 2.3 Lesion-Level Cost-Sensitive Classification Loss -- 2.4 Slice-Level Cost-Sensitive Classification Loss -- 3 Experiments and Evaluation -- 3.1 Data Set and Implementation Details -- 3.2 Evaluation Metrics -- 4 Results -- 4.1 Adjusting Mis-classification Cost at Lesion-Level -- 4.2 Adjusting Mis-classification Cost at Slice-Level -- 4.3 Adjusting Mis-classification Cost at Both Levels -- 4.4 Results Analysis -- 5 Conclusions -- References -- Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability -- 1 Introduction -- 2 Related Work -- 3 Materials -- 4 Method -- 4.1 Model Backbone -- 4.2 Spatial Attention -- 4.3 Single-Plane and Multi-plane Analysis -- 4.4 Training Pipeline -- 5 Evaluation -- 5.1 Quantitative -- 5.2 Ablation Study -- 6 Explainability -- 6.1 Localisation Ability -- 6.2 Features -- 6.3 Limitations -- 7 Conclusion -- References -- Improved Artifact Detection in Endoscopy Imaging Through Profile Pruning -- 1 Introduction -- 2 Proposed Method -- 2.1 Artifact Detection -- 2.2 Novel Pruning Method Using Instance Profiles -- 3 Results -- 3.1 Dataset -- 3.2 Evaluation Metrics -- 3.3 Experimental Setup -- 3.4 Quantitative Results -- 3.5 Qualitative Results -- 4 Discussion and Conclusion -- References -- Automatic Detection of Extra-Cardiac Findings in Cardiovascular Magnetic Resonance -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Data Pre-processing -- 3.2 Binary ECF Classification -- 3.3 Multi-label ECF Classification -- 3.4 Training -- 3.5 Statistics -- 4 Results -- 4.1 Binary ECF Classification -- 4.2 Multi-label ECF Classification -- 5 Discussion and Conclusion -- References -- Brain-Connectivity Analysis to Differentiate Phasmophobic and Non-phasmophobic: An EEG Study -- 1 Introduction -- 2 Principles and Methodologies -- 2.1 Classical CCM."]
[{"$nulls":4},"2.2 Estimating the Direction of Causation Using Conditional Entropy -- 2.3 Classification Using Kernelized Support Vector Machine -- 3 Experiments and Results -- 3.1 Experimental Setup -- 3.2 Data Preprocessing -- 3.3 Active Brain Region Selection Usings LORETA -- 3.4 Effective Connectivity Estimation by CCM Algorithm -- 3.5 Statistical Analysis Using One-Way ANOVA Test -- 3.6 Relative Performance Analysis of the Proposed CCM -- 4 Conclusion -- References -- Image Registration, and Reconstruction -- Virtual Imaging for Patient Information on Radiotherapy Planning and Delivery for Prostate Cancer -- 1 Introduction -- 2 Materials and Methods -- 2.1 Study Design -- 2.2 Eligibility and Exclusion Criteria -- 2.3 Radiotherapy -- 2.4 Bladder and Rectal Measurements -- 2.5 Bladder Volume Model -- 2.6 Statistical Analysis -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Data-Driven Speed-of-Sound Reconstruction for Medical Ultrasound: Impacts of Training Data Format and Imperfections on Convergence -- 1 Introduction -- 2 Methods -- 2.1 Data Simulation -- 2.2 Network Setup -- 3 Results and Discussion -- 3.1 Data Format -- 3.2 Decimation -- 4 Conclusion -- References -- Selective Motion Artefact Reduction via Radiomics and k-space Reconstruction for Improving Perivascular Space Quantification in Brain Magnetic Resonance Imaging -- 1 Introduction -- 2 Materials and Methods -- 2.1 Subjects, Magnetic Resonance Imaging and Clinical Visual Scores -- 2.2 Image Quality Assessment -- 2.3 Motion Artefact Reduction -- 2.4 PVS Segmentation -- 2.5 Comparison Against "]
[{"$nulls":4},"a Relevant Framework -- 2.6 Validation Against Clinical Parameters -- 3 Results -- 3.1 Image Quality Classification Results -- 3.2 Motion Artefact Reduction -- 3.3 Relationship Between Computational Measures and Clinical Visual Scores -- 4 Discussion -- References."]
[{"$nulls":4},"Mass Univariate Regression Analysis for Three-Dimensional Liver Image-Derived Phenotypes -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Image Analysis and Mesh Construction -- 2.3 Mass Univariate Regression Analysis -- 3 Results -- 4 Discussion and Conclusions -- References -- Automatic Re-orientation of 3D Echocardiographic Images in Virtual Reality Using Deep Learning -- 1 Introduction -- 1.1 Related Work -- 2 Methodology -- 2.1 Data and Labelling -- 2.2 Methods -- 3 Results -- 4 Applications -- 4.1 Scene Setup -- 4.2 Integration -- 5 Discussion -- References -- A Simulation Study to Estimate Optimum LOR Angular Acceptance for the Image Reconstruction with the Total-Body J-PET -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Conclusions -- References -- Optimised Misalignment Correction from Cine MR Slices Using Statistical Shape Model -- 1 Introduction -- 2 Preprocessing and Initial Misalignment Corrections -- 2.1 Preprocessing -- 2.2 Intensity and Contours Based Misalignment Corrections -- 3 Proposed Misalignment Correction Using Statistical Shape Model -- 3.1 Fitting the Statistical Shape Model -- 3.2 Misalignment Correction Using the SSM -- 4 Experimental Analysis -- 5 Conclusion -- References -- Slice-to-Volume Registration Enables Automated Pancreas MRI Quantification in UK Biobank -- 1 Introduction -- 2 Materials and Methods -- 2.1 UK Biobank Data -- 2.2 Slice-to-Volume Registration Method -- 2.3 SVR Implementation and Inference at Scale -- 2.4 Automated Quality Control -- 2.5 SVR Validation -- 3 Results -- 3.1 T1 Quantification: No Registration vs SVR-SSC -- 3.2 SVR Validation -- 4 Discussion and Conclusions -- References -- Image Segmentation -- Deep Learning-Based Landmark Localisation in the Liver for Couinaud Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Dataset -- 2.2 Landmark Localisation Model."]
[{"$nulls":4},"2.3 Direct Segmentation Model -- 2.4 Spatial Configuration Post-processing -- 2.5 Training and Evaluation -- 3 Results -- 3.1 Landmarking Accuracy -- 3.2 Couinaud Segmentation Accuracy -- 4 Discussion and Conclusion -- References -- Reproducibility of Retinal Vascular Phenotypes Obtained with Optical Coherence Tomography Angiography: Importance of Vessel Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Participant Demographics and Imaging Protocol -- 2.2 Image Analysis -- 2.3 Statistical Analysis -- 3 Results -- 3.1 Microvascular Phenotype Reproducibility over Repeated OCTA Imaging -- 3.2 Dependence of Microvascular Phenotypes on the Choice of Segmentation/Skeletonization Algorithm -- 4 Discussion -- References -- Fast Automatic Bone Surface Segmentation in Ultrasound Images Without Machine Learning -- 1 Introduction -- 2 Methods -- 2.1 Simplified Segmentation Method with Bone Probability Map -- 2.2 Image Acquisition and Hardware Pre-sets -- 2.3 Algorithm Testing -- 2.4 Performance Testing Against a Machine Learning Model -- 3 Results -- 3.1 Processing Time -- 3.2 Quantitative Comparison Between Methods -- 3.3 Qualitative Comparison Between Methods -- 3.4 Performance Comparison with U-Net -- 4 Discussion and Conclusion -- References -- Pancreas Volumetry in UK Biobank: Comparison of Models and Inference at Scale -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Acquisition -- 2.2 Data Labelling and Preprocessing -- 2.3 Model Architectures -- 2.4 Model Training and Testing -- 2.5 Model Inference at Scale -- 3 Results -- 3.1 Model Evaluation -- 3.2 Comparison with Volumetry from Pancreas-Specific Scan -- 3.3 UK Biobank Population Volumetry -- 3.4 Pancreas Volume "]
[{"$nulls":4},"Diurnal Variation. -- 4 Discussion and Conclusion -- References."]
[{"$nulls":4},"Ensemble of Deep Convolutional Neural Networks with Monte Carlo Dropout Sampling for Automated Image Segmentation Quality Control and Robust Deep Learning Using Small Datasets."]