11020nam 2200529 450 99646452810331620231110215849.03-030-80432-1(CKB)4100000011979517(MiAaPQ)EBC6676157(Au-PeEL)EBL6676157(OCoLC)1260344702(PPN)257358889(EXLCZ)99410000001197951720220327d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMedical 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]Cham, Switzerland :Springer,[2021]©20211 online resource (566 pages)Lecture Notes in Computer Science ;v.12722Includes index.3-030-80431-3 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 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.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.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 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.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.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 Diurnal Variation. -- 4 Discussion and Conclusion -- References.Ensemble of Deep Convolutional Neural Networks with Monte Carlo Dropout Sampling for Automated Image Segmentation Quality Control and Robust Deep Learning Using Small Datasets.Lecture Notes in Computer Science Diagnostic imagingCongressesDiagnostic imagingData processingCongressesDiagnostic imagingDiagnostic imagingData processing616.0754Papiez Bartłomiej W.MiAaPQMiAaPQMiAaPQBOOK996464528103316Medical Image Understanding and Analysis2186352UNISA