Bildverarbeitung für die Medizin 2019

Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck
 
 
Springer Vieweg (Verlag)
  • erschienen am 6. Februar 2019
  • |
  • XXII, 356 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Wasserzeichen-DRM | Systemvoraussetzungen
978-3-658-25326-4 (ISBN)
 
In den letzten Jahren hat sich der Workshop "Bildverarbeitung für die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2019 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - einige davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere Bildgebung und -akquisition, Maschinelles Lernen, Bildsegmentierung und Bildanalyse, Visualisierung und Animation, Zeitreihenanalyse, Computerunterstützte Diagnose, Biomechanische Modellierung, Validierung und Qualitätssicherung, Bildverarbeitung in der Telemedizin u.v.m.
weitere Ausgaben werden ermittelt
  • Intro
  • Bildverarbeitung für die Medizin 2019
  • Sponsoren des Workshops BVM 2019
  • Beste wissenschaftliche Arbeiten
  • Beste Präsentationen:
  • Bestes Poster
  • Vorwort
  • Inhaltsverzeichnis
  • 1 Abstract: Anchor-Constrained Plausibility. A Novel Concept for Assessing Tractography and Reducing False-Positives
  • References
  • 2 Automatic Detection of Blood Vessels in Optical Coherence Tomography Scans
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Methodology
  • 3 Results
  • 4 Discussion
  • References
  • 3 Prediction of Liver Function Based on DCE-CT
  • 1 Introduction
  • 2 Methods
  • 2.1 Dataset
  • 2.2 Pre-processing
  • 2.3 Model architecture and training
  • 2.4 Prediction generation and statistical evaluation
  • 3 Results
  • 4 Discussion
  • 5 Conclusion
  • References
  • 4 Abstract: Adversarial Examples as Benchmark for Medical Imaging Neural Networks
  • References
  • 5 Evaluation of Image Processing Methods for Clinical Applications. Mimicking Clinical Data Using Conditional GANs
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Style transfer using conditional generative adversarial networks
  • 2.2 Medical image style transfer
  • 2.3 Network architecture
  • 3 Results
  • 3.1 Data and setup
  • 3.2 Experiments and results
  • 4 Discussion and conclusion
  • References
  • 6 Abstract: Some Investigations on Robustness of Deep Learning in Limited Angle Tomography
  • References
  • 7 Abstract: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
  • References
  • 8 Deep Multi-Modal Encoder-Decoder Networks for Shape Constrained Segmentation and Joint Representation Learning
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Joint training
  • 2.2 Implementation details and experiments
  • 3 Results
  • 4 Conclusion
  • References
  • 9 Abstract: Fan-to-Parallel Beam Conversion. Deriving Neural Network Architectures Using Precision Learning
  • References
  • 10 Abstract: Tract Orientation Mapping for Bundle-Specific Tractography
  • References
  • 11 Segmentation of Vertebral Metastases in MRI Using an U-Net like Convolutional Neural Network
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Image data and pre-processing
  • 2.2 Network architecture
  • 2.3 Evaluation
  • 3 Results
  • 4 Discussion
  • References
  • 12 Synthetic Training with Generative Adversarial Networks for Segmentation of Microscopies
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Generative adversarial networks
  • 2.2 StackGAN
  • 2.3 Pix2pixHD GAN
  • 3 Results
  • 3.1 Generating label maps for esophagus
  • 3.2 Transfer of label maps to esophagus microscopy images
  • 3.3 Training a U-Net with synthetic image pairs
  • 4 Discussion
  • References
  • 13 Gradient-Based Expanding Spherical Appearance Models for Femoral Model Initialization in MRI
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 GESAM
  • 2.2 Evaluation
  • 3 Results
  • 4 Discussion
  • References
  • 14 Deep Segmentation Refinement with Result-Dependent Learning. A Double U-Net for Hip Joint Segmentation in MRI
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Deep segmentation refinement
  • 2.2 Evaluation
  • 3 Results
  • 4 Discussion
  • 4.1 Conclusion
  • References
  • 15 Abstract: Automatic Estimation of Cochlear Duct Length and Volume Size
  • References
  • 16 Interactive Neural Network Robot User Investigation for Medical Image Segmentation
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Random sampling over whole image (rand)
  • 2.2 Random sampling from GT (rand gt)
  • 2.3 Robot user by Kohli et al. (kohli12)
  • 2.4 Robot user by Xu et al. (xu16)
  • 2.5 Robot user by Wang et al. (wang17)
  • 3 Experiments
  • 4 Results
  • 5 Discussion and outlook
  • References
  • 17 Tracing of Nerve Fibers Through Brain Regions of Fiber Crossings in Reconstructed 3D-PLI Volumes
  • 1 Introduction
  • 2 Materials and methods
  • 3 Results
  • 4 Discussion
  • References
  • 18 Dilated Deeply Supervised Networks for Hippocampus Segmentation in MRI
  • 1 Introduction
  • 2 Methodology
  • 2.1 Data acquisition
  • 2.2 Training procedures
  • 3 Results and discussion
  • 4 Conclusion
  • References
  • 19 Automatic Detection and Segmentation of the Acute Vessel Thrombus in Cerebral CT
  • 1 Introduction
  • 1.1 Related work
  • 2 Methods
  • 2.1 Clot candidates segmentation
  • 2.2 Clot classification
  • 3 Experiments
  • 3.1 Data
  • 3.2 Results
  • 4 Discussion
  • References
  • 20 Sparsely Connected Convolutional Layers in CNNs for Liver Segmentation in CT
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Network
  • 2.2 Data
  • 2.3 Training
  • 2.4 Experimental design and evaluation metrics
  • 3 Results
  • 3.1 LiTS
  • 3.2 IRCAD
  • 4 Discussion
  • References
  • 21 Smooth Ride: Low-Pass Filtering of Manual Segmentations Improves Consensus
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Data acquisition and segmentation
  • 2.2 Data processing
  • 3 Results
  • 4 Discussion
  • References
  • 22 User Loss. A Forced-Choice-Inspired Approach to Train Neural Networks Directly by User Interaction
  • 1 Introduction
  • 2 Methods
  • 2.1 Subband decomposition
  • 2.2 Soft-thresholding
  • 2.3 Neural network
  • 2.4 User loss
  • 3 Experiments and results
  • 3.1 Qualitative results
  • 3.2 Quantitative evaluation
  • 4 Conclusion and discussion
  • References
  • 23 Sodium Image Denoising Based on a Convolutional Denoising Autoencoder
  • 1 Introduction
  • 2 Methods
  • 2.1 Denoising autoencoder
  • 2.2 Training
  • 3 Evaluation
  • 3.1 Materials
  • 3.2 Results
  • 4 Discussion
  • References
  • 24 Improved X-Ray Bone Segmentation by Normalization and Augmentation Strategies
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Formulation of input and feature normalization
  • 2.2 Data augmentation strategies
  • 2.3 Dataset
  • 2.4 Ablation study and performance metrics
  • 3 Results
  • 4 Discussion
  • References
  • 25 Multi-Modal Super-Resolution with Deep Guided Filtering
  • 1 Introduction
  • 2 Methods
  • 2.1 The guided filter
  • 2.2 End-to-end trainable guided filter
  • 3 Experiments
  • 4 Results
  • 5 Discussion
  • 6 Conclusion
  • References
  • 26 Semi-Automatic Cell Correspondence Analysis Using Iterative Point Cloud Registration
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Cell stretching and image acquisition
  • 2.2 Semi-automatic cell correspondence analysis
  • 3 Results
  • 4 Discussion
  • References
  • 27 Pediatric Patient Surface Model Atlas Generation and X-Ray Skin Dose Estimation
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 RGBD imaging setup and data acquisition
  • 2.2 Preprocessing
  • 2.3 Template fitting
  • 2.4 Atlas generation
  • 2.5 Skin entrance dose estimation
  • 3 Evaluation and results
  • 4 Discussion
  • References
  • 28 Blind Rigid Motion Estimation for Arbitrary MRI Sampling Trajectories
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Rigid motion model
  • 2.2 Nonequidistant sampling scheme
  • 2.3 Regularization
  • 2.4 Motion estimation gradient
  • 2.5 Image reconstruction
  • 2.6 Test setup
  • 3 Results
  • 4 Discussion
  • References
  • 29 Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency
  • 1 Introduction
  • 2 CIV look-up-table
  • 3 Student's t-distribution-based maximum likelihood estimation for consistency optimization
  • 4 Experiments and results
  • 4.1 Experimental Setup
  • 4.2 Motion Compensation
  • 4.3 Results
  • 5 Conclusion
  • References
  • 30 Retrospective Blind MR Image Recovery with Parametrized Motion Models
  • 1 Introduction
  • 2 Methods
  • 2.1 Optimization scheme
  • 2.2 Image reconstruction
  • 2.3 Motion update
  • 2.4 Test setup
  • 3 Results
  • 4 Discussion
  • References
  • 31 Model-Based Motion Artifact Correction in Digital Subtraction Angiography Using Optical-Flow
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Image formation and noise model
  • 2.2 Deformable registration of mask and fill images
  • 2.3 Material
  • 3 Results
  • 4 Discussion and conclusion
  • References
  • 32 MedicVR. Acceleration and Enhancement Techniques for Direct Volume Rendering in Virtual Reality
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Lens matched shading for direct volume rendering
  • 2.2 Volume clipping with the virtual reality plane-tool
  • 2.3 Shadow ray diffuse culling (SRDC)
  • 2.4 Semi-adaptive sampling
  • 3 Results
  • 4 Discussion
  • References
  • 33 Efficient Web-Based Review for Automatic Segmentation of Volumetric DICOM Images
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Architecture and tools
  • 2.2 Segmentation evaluation
  • 2.3 Experiment
  • 3 Results
  • 4 Discussion
  • References
  • 34 Abstract: Phase-Sensitive Region-of-Interest Computed Tomography
  • References
  • 35 Joint Multiresolution and Background Detection Reconstruction for Magnetic Particle Imaging
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Multiresolution reconstruction
  • 2.2 Background detection
  • 2.3 Test setup
  • 3 Results
  • 4 Discussion
  • References
  • 36 Abstract: Double Your Views: Exploiting Symmetry in Transmission Imaging
  • References
  • 37 3D-Reconstruction of Stiff Wires from a Single Monoplane X-Ray Image
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Data
  • 2.2 2D segmentation
  • 2.3 Estimation of a virtual projection
  • 2.4 Reconstruction from real and virtual views
  • 3 Results
  • 4 Discussion and conclusion
  • References
  • 38 Regularized Landmark Detection with CAEs for Human Pose Estimation in the Operating Room
  • 1 Introduction
  • 1.1 Related work
  • 1.2 Contribution
  • 2 Materials and methods
  • 2.1 Pose estimation
  • 2.2 Pose regularization
  • 3 Results
  • 3.1 Pose estimation
  • 3.2 Pose regularization
  • 4 Discussion
  • References
  • 39 Abstract: Does Bone Suppression and Lung Detection Improve Chest Disease Classification?
  • References
  • 40 Towards Automated Reporting and Visualization of Lymph Node Metastases of Lung Cancer
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Structure segmentation
  • 2.2 Vertex mesh generation
  • 3 Results
  • 4 Discussion
  • References
  • 41 Workflow Phase Detection in Fluoroscopic Images Using Convolutional Neural Networks
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Data
  • 2.2 Preprocessing
  • 2.3 Data composition
  • 2.4 CNN architectures
  • 3 Results
  • 4 Discussion
  • References
  • 42 Abstract: Interpretable Explanations of Black Box Classifiers Applied on Medical Images by Meaningful Perturbations Using Variational Autoencoders
  • References
  • 43 Abstract: Deep Transfer Learning for Aortic Root Dilation Identification in 3D Ultrasound Images
  • References
  • 44 Abstract: Leveraging Web Data for Skin Lesion Classification
  • References
  • 45 Machbarkeitsstudie zur CNN-basierten Identifikation und TICI-Klassifizierung zerebraler ischämischer Infarkte in DSA-Daten
  • 1 Einleitung
  • 2 Material und Methoden
  • 2.1 Verwendete Bilddaten
  • 2.2 Bilddatenvorverarbeitung
  • 2.3 CNN-Architektur und Datenverarbeitung
  • 2.4 Experimente und Auswertestrategie
  • 3 Ergebnisse
  • 3.1 Paarweise Differenzierung von TICI-Scores
  • 3.2 Differenzierung aller TICI-Scores
  • 3.3 Differenzierung nach Infarktlokalisation
  • 4 Diskussion
  • Literaturverzeichnis
  • 46 Image-Based Detection of MRI Hardware Failures
  • 1 Introduction
  • 1.1 Hardware failures
  • 2 Materials and methods
  • 2.1 Image quality metrics
  • 2.2 Evaluation methods
  • 3 Results
  • 4 Discussion
  • References
  • 47 Detection of Unseen Low-Contrast Signals Using Classic and Novel Model Observers
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Data set
  • 2.2 Classic model observer
  • 2.3 Neural network architecture and training strategy
  • 3 Results
  • 4 Discussion
  • References
  • 48 Abstract: Imitating Human Soft Tissue with Dual-Daterial 3D Printing
  • References
  • 49 A Mixed Reality Simulation for Robotic Systems
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Desktop and augmented reality visualization
  • 2.2 Room capture
  • 3 Results
  • 4 Discussion
  • References
  • 50 Image Quality Assessments
  • 1 Introduction
  • 2 Materials and methods
  • 3 Results
  • 4 Discussion
  • References
  • 51 Abstract: HoloLens. Streaming of 3D Data from Ultrasound Systems to Augmented Reality Glasses
  • References
  • 52 Open-Source Tracked Ultrasound with Anser Electromagnetic Tracking
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Tracked ultrasound setup
  • 2.2 Standardized assessment of tracked ultrasound
  • 3 Results
  • 4 Discussion
  • References
  • 53 Navigierte Interventionen im Kopf- und Halsbereich. Standardisiertes Assessment eines neuen, handlichen Feldgenerators
  • 1 Einleitung
  • 2 Methoden
  • 3 Ergebnisse
  • 4 Diskussion
  • Literaturverzeichnis
  • 54 Abstract: Multispectral Imaging Enables Visualization of Spreading Depolarizations in Gyrencephalic Brain
  • References
  • 55 Combining Ultrasound and X-Ray Imaging for Mammography. A Prototype Design
  • 1 Introduction
  • 2 Methods and materials
  • 2.1 Hardware prototype
  • 2.2 Software prototype
  • 2.3 Geometric mapping
  • 3 Results
  • 4 Conclusion
  • References
  • 56 Towards In-Vivo X-Ray Nanoscopy. Acquisition Parameters vs. Image Quality
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 X-ray physics and photon energy considerations
  • 2.2 Compressive sensing
  • 2.3 Experimental setup
  • 3 Results
  • 4 Discussion
  • References
  • 57 Abstract: Beamforming Sub-Sampled Raw Ultrasound Data with DeepFormer
  • References
  • 58 Shape Sensing with Fiber Bragg Grating Sensors. A Realistic Model of Curvature Interpolation for Shape Reconstruction
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Shape reconstruction
  • 2.2 Averaging cubic interpolation
  • 2.3 Experimental setup
  • 3 Results
  • 4 Discussion
  • References
  • 59 On the Characteristics of Helical 3D X-Ray Dark-Field Imaging
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Dark-field with 2D trajectories
  • 2.2 Dark-field with a 3D helical trajectory
  • 3 Experiments and results
  • 3.1 Experiment 1
  • 3.2 Experiment 2
  • 3.3 Experiment 3
  • 4 Discussion
  • References
  • 60 Effects of Tissue Material Properties on X-Ray Image, Scatter and Patient Dose. A Monte Carlo Simulation
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Phantom model geometry and material parameters
  • 2.2 Detector model
  • 2.3 Simulation of experimental setup
  • 3 Results
  • 3.1 Scatter and dose distributions
  • 3.2 X-ray images
  • 4 Summary
  • References
  • 61 Isocenter Determination from Projection Matrices of a C-Arm CBCT
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Background: the projection matrix
  • 2.2 Isocenter computation methods
  • 2.3 Experiments
  • 3 Results
  • 4 Discussion and conclusion
  • References
  • 62 Improving Surgical Training Phantoms by Hyperrealism. Deep Unpaired Image-to-Image Translation from Real Surgeries
  • References
  • 63 Evaluation of Spatial Perception in Virtual Reality within a Medical Context
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Glyph design
  • 2.2 Heatmap design
  • 2.3 Pseudo-chromadepth design
  • 2.4 Experiment
  • 3 Results
  • 4 Discussion
  • References
  • 64 Simulation von Radiofrequenzablationen für die Leberpunktion in 4D-VR-Simulationen
  • 1 Einleitung
  • 2 Material und Methoden
  • 2.1 Randbedingungen
  • 2.2 Berücksichtigung der Atembewegung
  • 2.3 Evaluationsmethode
  • 3 Ergebnisse
  • 3.1 Simulation der Temperaturausbreitung
  • 3.2 Rendering-Performance
  • 4 Diskussion
  • Literaturverzeichnis
  • 65 Abstract: An SVR-Based Data-Driven Leaflet Modeling Approach for Personalized Aortic Valve Prosthesis Development
  • References
  • 66 Mitral Valve Quantification at a Glance. Flattening Patient-Specific Valve Geometry
  • 1 Introduction
  • 2 Materials and methods
  • 3 Evaluation
  • 4 Results
  • 5 Discussion
  • References
  • 67 Fully-Deformable 3D Image Registration in Two Seconds
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 Model
  • 2.2 Parallelization
  • 3 Results
  • 3.1 Radboud follow-up CT dataset
  • 3.2 DIR-Lab 4DCT benchmark
  • 4 Discussion
  • References
  • 68 Abstract: Landmark-Free Initialization of Multi-Modal Image Registration
  • References
  • 69 Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking
  • 1 Introduction
  • 1.1 Prior work on CNN-Based deformable registration
  • 1.2 Contributions
  • 2 Materials and methods
  • 2.1 Architecture and training
  • 2.2 Experiments
  • 3 Results
  • 4 Discussion
  • References
  • 70 Respiratory Deformation Estimation in X-Ray-Guided IMRT Using a Bilinear Model
  • 1 Introduction
  • 2 Material and methods
  • 2.1 Respiratory motion models
  • 2.2 Bilinear model for rotational X-ray
  • 2.3 Performance comparison
  • 3 Results
  • 4 Discussion
  • References
  • 71 Augmented Mitotic Cell Count Using Field of Interest Proposal
  • 1 Introduction
  • 2 Material
  • 3 Methods
  • 3.1 Mitosis as segmentation task
  • 3.2 High power field area proposal
  • 3.3 Convolutional neural network (CNN) structure
  • 4 Results
  • 5 Discussion
  • References
  • 72 Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks
  • 1 Introduction
  • 2 Methods
  • 2.1 Dataset
  • 2.2 Models and training
  • 3 Results
  • 4 Discussion
  • References
  • 73 Efficient Construction of Geometric Nerve Fiber Models for Simulation with 3D-PLI
  • 1 Introduction
  • 2 Material and methods
  • 3 Results
  • 4 Discussion
  • References
  • 74 Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis
  • 1 Introduction
  • 2 Materials and methods
  • 2.1 PAMONO sensor
  • 2.2 deepRacin
  • 2.3 Modified image processing pipeline
  • 2.4 Spectral and wavelet feature extraction
  • 2.5 Training and classification
  • 3 Results
  • 3.1 Classification performance
  • 3.2 Execution time
  • 4 Discussion
  • References
  • 75 Black-Box Hyperparameter Optimization for Nuclei Segmentation in Prostate Tissue Images
  • 1 Introduction
  • 2 Methods
  • 2.1 Segmentation pipelines
  • 2.2 HyperHyper optimization framework
  • 3 Experimental results
  • 4 Conclusion
  • References
  • Kategorisierung der Beiträge
  • Autorenverzeichnis
DNB DDC Sachgruppen
Dewey Decimal Classfication (DDC)

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