
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017
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The 255 revised full papers presented were carefully reviewed and selected from 800 submissions in a two-phase review process. The papers have been organized in the following topical sections: Part I: atlas and surface-based techniques; shape and patch-based techniques; registration techniques, functional imaging, connectivity, and brain parcellation; diffusion magnetic resonance imaging (dMRI) and tensor/fiber processing; and image segmentation and modelling. Part II: optical imaging; airway and vessel analysis; motion and cardiac analysis; tumor processing; planning and simulation for medical interventions; interventional imaging and navigation; and medical image computing. Part III: feature extraction and classification techniques; and machine learning in medical image computing.
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Content
- Intro
- Preface
- Organization
- Contents - Part II
- Optical Imaging
- Cell Lineage Tracing in Lens-Free Microscopy Videos
- 1 Introduction
- 2 Methods
- 2.1 Lineage Tracing
- 2.2 Cell Detection with Residual Networks
- 3 Experiments and Results
- 4 Conclusions
- References
- Separation of Transmitted Light and Scattering Components in Transmitted Microscopy
- 1 Introduction
- 2 High-Frequency Illumination Microscopy Method
- 2.1 Overlapping Light Rays Leading to Unclearness
- 2.2 Separation of Direct and Global Component
- 3 Experimental Results
- 3.1 Measuring System
- 3.2 Spatial Multi-spectral Absorption Analysis
- 4 Conclusion
- References
- Neuron Segmentation Using Deep Complete Bipartite Networks
- 1 Introduction
- 2 Methodology
- 2.1 CB-Net
- 2.2 Leveraging Approximate Instance-Wise Annotation
- 2.3 Implementation Details
- 3 Experiments
- 4 Conclusions
- References
- Cell Encoding for Histopathology Image Classification
- 1 Introduction
- 2 Methodology
- 2.1 Semi-supervised Kernel Discrete Hashing
- 2.2 Hash Codes Based Classification
- 3 Experiments
- 3.1 Experimental Setting
- 3.2 Results and Analysis
- 4 Conclusion
- References
- Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging
- Abstract
- 1 Introduction
- 2 Materials and Methods
- 2.1 Interleaved SL and WL Views
- 2.2 Tissue Surface Feature Tracking and Shape Measurement
- 2.3 Super-Spectral-Resolution Imaging
- 3 Experimental Results
- 4 Discussion and Conclusion
- Ethics Statement
- References
- Computational Immunohistochemistry: Recipes for Standardization of Immunostaining
- 1 Introduction
- 2 Proposed Methodology
- 2.1 Image Segmentation and Representation
- 2.2 Staining Quality Metric (SQM) Learning
- 2.3 Analysis of Sensitivity to Process Parameters
- 3 Experimental Validation
- 4 Conclusions
- References
- Two-Stream Bidirectional Long Short-Term Memory for Mitosis Event Detection and Stage Localization in Phase-Contrast Microscopy Images
- 1 Introduction
- 1.1 Related Work
- 1.2 Motivation and Contributions
- 2 Methodology
- 2.1 Candidate Sequence Extraction
- 2.2 Two-Stream Bidirectional Long-Short Term Memory
- 3 Experiments
- 3.1 Dataset
- 3.2 Evaluation Metric
- 3.3 Validation on the Proposed Architecture
- 3.4 Comparisons on the Mitosis Event Detection
- 3.5 Comparisons on the Mitosis Stage Localization
- 4 Conclusion
- References
- Refocusing Phase Contrast Microscopy Images
- 1 Introduction
- 1.1 Related Work
- 1.2 Our Proposal
- 2 Methodology
- 2.1 Refocusing Images from the Optics Perspective
- 2.2 Refocusing Images from the Specimen Perspective
- 3 Experimental Results
- 4 Conclusion
- A Appendix
- References
- Semi-supervised Segmentation of Optic Cup in Retinal Fundus Images Using Variational Autoencoder
- 1 Introduction
- 2 Proposed Semi-supervised Segmentation Method
- 2.1 GVAE: Generative Variational Autoencoder
- 2.2 SVAE: Segmentation Variational Autoencoder
- 3 Experiment Results
- 4 Conclusion
- References
- QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data
- 1 Introduction
- 2 Proposed Spatio-Temporal Denoising Algorithm
- 2.1 Noise Model and Energy Minimization Formulation
- 2.2 Quantile Sparse Image Prior
- 2.3 Alternating Direction Method of Multiplier Optimization
- 3 Experiments and Results
- 4 Conclusion
- References
- Fast Background Removal Method for 3D Multi-channel Deep Tissue Fluorescence Imaging
- 1 Introduction
- 2 Method
- 2.1 Background Model
- 2.2 Multi-channel Image
- 2.3 Assumption
- 2.4 Algorithm
- 3 Experiments and Results
- 4 Conclusion
- References
- Fast Estimation of Haemoglobin Concentration in Tissue Via Wavelet Decomposition
- 1 Introduction
- 2 Method
- 3 Experiments and Results
- 3.1 Comparison with Hardware Multispectral Imaging Signals
- 3.2 Experiments on in Vivo Data from a Stereo-Laparoscope
- 4 Conclusion
- References
- Efficient Reconstruction of Holographic Lens-Free Images by Sparse Phase Recovery
- 1 Introduction
- 2 Basics of Holographic Imaging
- 3 Sparse Phase Recovery
- 4 Multi-depth Reconstructions
- 5 Conclusions
- References
- Quality Assessment of Retinal Hyperspectral Images Using SURF and Intensity Features
- 1 Introduction
- 2 Dataset
- 3 Method
- 3.1 Parasitic Signal Detection
- 3.2 Movement Detection
- 3.3 Defocus Detection
- 3.4 Illumination Detection
- 3.5 Quality Index Computation
- 4 Results and Discussion
- 5 Conclusion
- References
- Isotropic Reconstruction of 3D Fluorescence Microscopy Images Using Convolutional Neural Networks
- 1 Introduction
- 2 Methods
- 2.1 Restoration via Convolutional Neural Networks
- 2.2 Network Architecture and Training
- 3 Results
- 3.1 Synthetic Data
- 3.2 Real Data
- 4 Discussion
- References
- Deep Learning for Isotropic Super-Resolution from Non-isotropic 3D Electron Microscopy
- 1 Introduction
- 2 Related Work
- 3 Architecture Design
- 3.1 3D Anisotropic FSRCNN
- 3.2 3D Super-Resolution U-Net
- 4 Experiments
- 4.1 Implementation Details
- 4.2 3D Anisotropic FSRCNN
- 4.3 3D Super-Resolution U-Net
- 4.4 Comparison/Results
- 5 Conclusion
- References
- Histological Detection of High-Risk Benign Breast Lesions from Whole Slide Images
- 1 Introduction
- 2 Methodology
- 2.1 Stain Color Normalization
- 2.2 Ductal ROI Segmentation
- 2.3 Cytological Phenotyping
- 2.4 Architectural Phenotyping
- 3 Experiments and Results
- 3.1 Dataset
- 3.2 Results
- 4 Conclusion and Discussions
- References
- Accurate Correspondence of Cone Photoreceptor Neurons in the Human Eye Using Graph Matching Applied to Longitudinal Adaptive Optics Images
- 1 Introduction
- 2 Methodology
- 2.1 Longitudinal Matching of Cone Photoreceptor Neurons
- 2.2 Data Collection and Validation Method
- 3 Experimental Results
- 3.1 Validation Dataset
- 3.2 Test Dataset
- 4 Conclusion and Future Work
- References
- Airway and Vessel Analysis
- Active Learning and Proofreading for Delineation of Curvilinear Structures
- 1 Introduction
- 2 Attention Mechanism
- 2.1 Graph-Based Delineation
- 2.2 Error Detection
- 3 Active Learning and Proofreading
- 4 Results
- 4.1 Active Learning
- 4.2 Proofreading
- 4.3 Complete Pipeline
- 5 Conclusions
- References
- Tracking the Aortic Lumen Geometry by Optimizing the 3D Orientation of Its Cross-sections
- 1 Introduction
- 2 Energy Model to Compute the Orientation of the Cross-sectional Plane
- 3 Experimental Results
- 4 Conclusions
- References
- BRANCH:Bifurcation Recognition for Airway Navigation based on struCtural cHaracteristics
- 1 Introduction
- 2 Method
- 2.1 Detection of Airway Bifurcations on Depth Maps
- 2.2 Airway Bifurcation Representation
- 2.3 Camera Localisation
- 3 Results
- 4 Conclusion
- References
- Anatomy-Driven Modelling of Spatial Correlation for Regularisation of Arterial Spin Labelling Images
- 1 Introduction
- 2 Methods
- 2.1 Arterial Spin Labelling
- 2.2 Spatial Regularisation
- 2.3 Anatomy-Driven Modelling
- 2.4 Validation
- 3 Results
- 3.1 Test-Retest Reproducibility
- 3.2 Qualitative Image Validation
- 3.3 Group Statistics
- 4 Discussion and Conclusions
- References
- Tracking and Segmentation of the Airways in Chest CT Using a Fully Convolutional Network
- 1 Introduction
- 2 Method
- 2.1 3D U-Net Based Airway Tracking Algorithm
- 2.2 VOI Placement
- 2.3 Airway Tracking Algorithm
- 3 Experiment and Results
- 4 Discussion
- 5 Conclusion
- References
- Automatic Labeling of Vascular Structures with Topological Constraints via HMM
- 1 Introduction
- 2 Method
- 3 Experiments and Results
- 4 Conclusions
- References
- Biomechanical Patient-Specific Model of the Respiratory System Based on 4D CT Scans and Controlled by Personalized Physiological Compliance
- 1 Introduction
- 2 Materials and Methods
- 2.1 3D Segmentation and Reconstruction
- 2.2 Dynamic Biomechanical Patient-Specific Model of the Respiratory System
- 2.3 The Boundary Conditions (BC)
- 3 Results and Experimental Validation
- 4 Conclusion
- References
- CT Image Enhancement for Feature Detection and Localization
- 1 Introduction
- 2 Materials and Methods
- 2.1 Feature Likelihood Detection and Localization
- 2.2 Training Set Definition
- 2.3 Experimental Setup
- 3 Results
- 4 Conclusion
- References
- Information Theoretic Measurement of Blood Flow Complexity in Vessels and Aneurysms: Interlacing Complexity Index
- 1 Introduction
- 2 Interlacing Complexity Index
- 2.1 Natural Distribution of Streamlines
- 2.2 Scale-Dependent Mutual Information
- 2.3 Interlacing Complexity Index
- 3 Numerical Estimation of ICI in Aneurysms
- 3.1 Blood Flow Simulation from Patient-Specific Vasculatures
- 3.2 Inlet and Outlet Selection
- 3.3 Streamlines Generation and ICI Estimation
- 4 Experiments
- 4.1 Algorithm Accuracy and Precision Evaluation
- 4.2 Distribution of ICI in the Population of Aneurysms
- 4.3 Comparison with Subjective Flow Complexity and Stability
- 5 Conclusions
- References
- Globally-Optimal Anatomical Tree Extraction from 3D Medical Images Using Pictorial Structures and Minimal Paths
- 1 Introduction
- 2 Methodology
- 2.1 Bifurcation Detection
- 2.2 Branch Centerline Extraction
- 3 Experiments
- 3.1 Data Description
- 3.2 Evaluation Measures
- 3.3 Experimental Results
- 4 Conclusions
- References
- Motion and Cardiac Analysis
- Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation
- 1 Introduction
- 1.1 Related Works
- 2 Methods
- 2.1 Semi-supervised Learning
- 2.2 Conditional Random Field (CRF)
- 2.3 Network Architecture
- 3 Experiments and Results
- 3.1 Data and Pre-processing
- 3.2 Parameters
- 3.3 Evaluation of Segmentation Performance
- 4 Conclusion and Discussion
- References
- A Localized Statistical Motion Model as a Reproducing Kernel for Non-rigid Image Registration
- 1 Introduction
- 2 Background
- 3 Method
- 3.1 Statistical Motion Model
- 3.2 Model Construction
- 4 Experiments
- 5 Conclusion
- References
- Efficient Deformable Motion Correction for 3-D Abdominal MRI Using Manifold Regression
- Abstract
- 1 Introduction
- 2 Methodology
- 2.1 K-space Data Acquisition
- 2.2 Manifold Embedding and Clustering
- 2.3 Motion Field Estimation
- 2.4 Motion-Corrected Volume Reconstruction
- 3 Experiments and Results
- 3.1 Materials
- 3.2 Results
- 4 Conclusion and Discussions
- Acknowledgements
- References
- Flow Network Based Cardiac Motion Tracking Leveraging Learned Feature Matching
- 1 Introduction
- 2 Methods
- 2.1 Flow Network Based Tracking (FNT)
- 2.2 Learning Features and Metric Using Neural Network
- 3 Data and Results
- 3.1 Evaluation on Synthetic Data
- 3.2 Validation in Animal Study
- 4 Conclusion
- References
- Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
- 1 Introduction
- 2 Method
- 2.1 Dense Connection
- 2.2 The Architecture of DenseVoxNet
- 2.3 Training Procedure
- 3 Experiments and Results
- 4 Discussion and Conclusion
- References
- Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
- 1 Introduction
- 2 Method
- 3 Experiments and Results
- References
- Detection and Characterization of the Fetal Heartbeat in Free-hand Ultrasound Sweeps with Weakly-supervised Two-streams Convolutional Networks
- 1 Introduction
- 2 Materials and Methods
- 3 Experiments and Results
- 4 Conclusion
- References
- Retrospective Head Motion Estimation in Structural Brain MRI with 3D CNNs
- 1 Introduction
- 2 Methods
- 2.1 Voxel Classifier
- 2.2 Computation of the Measure of Head Motion
- 3 Experiments and Results
- 3.1 MRI Data and Manual Annotations
- 3.2 Experimental Setup
- 3.3 Results
- 4 Discussion
- References
- Learning-Based Spatiotemporal Regularization and Integration of Tracking Methods for Regional 4D Cardiac Deformation Analysis
- 1 Introduction
- 2 Method
- 2.1 Initial Tracking Methods
- 2.2 Spatiotemporal Displacement Regularization Learning
- 2.3 Soft-Threshold Outlier Regularization
- 2.4 Combining Complementary Methods via Multiview Learning
- 3 Experiments and Results
- 3.1 Quantitative Results
- 3.2 Qualitative Results
- 4 Conclusions
- References
- Fully Automated Segmentation-Based Respiratory Motion Correction of Multiplanar Cardiac Magnetic Resonance Images for Large-Scale Datasets
- 1 Introduction
- 2 Materials and Methods
- 2.1 Image Data
- 2.2 Segmentation Network
- 2.3 Multiplanar Registration Algorithm
- 3 Experiments and Results
- 3.1 Segmentation
- 3.2 Motion Correction
- 4 Discussion and Conclusions
- References
- Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video
- 1 Introduction
- 1.1 Related Work
- 2 Temporal Fetal Heart Network
- 2.1 Circular Anchor Mechanism
- 2.2 Intersection over Union (IoU) and Cosine Loss Functions
- 2.3 Temporally Recurrent Network
- 3 Implementation Details
- 4 Experiments, Results and Discussions
- 5 Conclusions
- References
- Longitudinal Analysis Using Personalised 3D Cardiac Models with Population-Based Priors: Application to Paediatric Cardiomyopathies
- 1 Introduction
- 2 Clinical Data
- 3 Personalised Cardiac Modeling
- 3.1 3D Electromechanical Cardiac Model
- 3.2 Parameter Estimation with Priors
- 3.3 Efficient Multi-fidelity Optimization
- 4 Results
- 4.1 Application to Longitudinal Analysis of the Cardiac Function
- 5 Conclusion and Discussion
- References
- Temporal Interpolation of Abdominal MRIs Acquired During Free-Breathing
- 1 Introduction
- 2 Method
- 3 Experiments and Results
- 4 Conclusion
- References
- Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks
- Abstract
- 1 Introduction
- 2 Patient-Specific Organ Motion Model Generation
- 3 Combining Generative Adversarial Networks
- 4 Experiments and Results
- 5 Discussion
- References
- CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-view CNN
- 1 Introduction
- 2 Proposed Multi-view Convolutional Neural Network (CNN) Architecture
- 3 Experimental Results
- 4 Discussions and Concluding Remarks
- References
- Analysis of Periodicity in Video Sequences Through Dynamic Linear Modeling
- 1 Introduction
- 2 Methods
- 2.1 Dynamic Linear Models
- 2.2 Experiments
- 3 Results
- 3.1 Natural Video
- 3.2 Ultrasound
- 3.3 MRI
- 4 Discussion and Conclusion
- References
- Tumor Processing
- Groupwise Registration of MR Brain Images Containing Tumors via Spatially Constrained Low-Rank Based Image Recovery
- Abstract
- 1 Introduction
- 2 Method
- 2.1 The Spatial Constraint
- 2.2 Spatially Constrained Low-Rank Based Image Recovery
- 3 Results
- 3.1 Evaluation of Dataset I
- 3.2 Evaluation of Dataset II
- 4 Conclusion
- Acknowledgements
- References
- Deep Correlational Learning for Survival Prediction from Multi-modality Data
- 1 Introduction
- 2 Methodology
- 3 Experiments
- 3.1 Dataset Description
- 3.2 Comparison Methods
- 3.3 Results and Discussion
- 4 Conclusion
- References
- Compressed Sensing on Multi-pinhole Collimator SPECT Camera for Sentinel Lymph Node Biopsy
- 1 Introduction
- 2 Materials and Methods
- 3 Results
- 4 Conclusion
- References
- Personalized Pancreatic Tumor Growth Prediction via Group Learning
- 1 Introduction
- 2 Group Learning Approach for Tumor Growth Prediction
- 2.1 Image Processing and Patch Extraction
- 2.2 Learning a Voxel-Wise Deep Representation
- 2.3 Learning a Predictive Model with Multi-source Features
- 3 Experiments and Results
- 4 Conclusion
- References
- Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation
- 1 Introduction
- 2 Variant of FCN
- 3 Boundary-Aware FCN
- 4 Evaluation
- 4.1 Results on BRATS15 Dataset
- 4.2 Results on BRATS13 Dataset
- 5 Conclusion
- References
- Field Effect Induced Organ Distension (FOrge) Features Predicting Biochemical Recurrence from Pre-treatment Prostate MRI
- 1 Introduction
- 2 Methods
- 2.1 Brief Overview
- 2.2 Creation of BCR+ and BCR- Cohort Atlases
- 2.3 Spatially Contextual Surface of Interest (SOI)
- 2.4 Field Effect Induced Organ Distension (FOrge) Features
- 3 Experimental Results and Discussions
- 3.1 Data Description and Preprocessing
- 3.2 Evaluation of Atlas Construction
- 3.3 Evaluation of FOrge Features from SOI vs Entire Capsule
- 4 Conclusions
- References
- Multi-label Inductive Matrix Completion for Joint MGMT and IDH1 Status Prediction for Glioma Patients
- Abstract
- 1 Introduction
- 2 Materials, Preprocessing, and Feature Extraction
- 3 MIMC-Based MGMT and IDH1 Status Prediction
- 3.1 Multi-label Transductive Matrix Completion (MTMC)
- 3.2 Multi-label Inductive Matrix Completion (MIMC)
- 3.3 Optimization Algorithm for MIMC
- 4 Results and Discussions
- 5 Conclusion
- Acknowledgments
- References
- Radiographic-Deformation and Textural Heterogeneity (r-DepTH): An Integrated Descriptor for Brain Tumor Prognosis
- 1 Introduction
- 2 Previous Work and Novel Contributions
- 3 Methodology
- 3.1 Notation
- 3.2 Radiographic-Deformation and Textural Heterogeneity (r-DepTH) Descriptor
- 4 Experimental Design
- 4.1 Data Description and Preprocessing
- 4.2 Implementation Details
- 5 Results and Discussion
- 5.1 Distinguishing LTS vs STS Using r-DepTH
- 5.2 Evaluation on Independent Validation Set
- 6 Concluding Remarks
- References
- RADIomic Spatial TexturAl descripTor (RADISTAT): Characterizing Intra-tumoral Heterogeneity for Response and Outcome Prediction
- 1 Introduction
- 2 Previous Work and Novel Contributions
- 3 Methodology
- 4 Experimental Design
- 4.1 Data Description
- 4.2 Implementation Details
- 5 Results
- 5.1 Distinguishing Treatment Response in Rectal Cancer
- 5.2 Predicting Overall Survival in GBMs
- 6 Concluding Remarks
- References
- Planning and Simulation for Medical Interventions
- Locally Affine Diffeomorphic Surface Registration for Planning of Metopic Craniosynostosis Surgery
- Abstract
- 1 Introduction
- 2 Methods
- 2.1 Template-Based Bone Cut Labeling
- 2.2 Poly-affine Transformation Model
- 2.3 Locally Affine Regions
- 2.4 Surface-Based Optimization
- 2.5 Surface Preservation and Dissimilarity Measure
- 3 Experiments
- 4 Results
- 5 Conclusion
- Acknowledgements
- References
- A Time Saver: Optimization Approach for the Fully Automatic 3D Planning of Forearm Osteotomies
- Abstract
- 1 Introduction
- 2 Methods
- 2.1 Multi-stage Optimization
- 2.2 Fitness Functions
- 3 Results and Discussion
- 4 Conclusion
- Acknowledgments
- References
- DARWIN: Deformable Patient Avatar Representation With Deep Image Network
- 1 Introduction
- 2 Method
- 2.1 Body Pose Classification and Landmark Detection
- 2.2 Patient Centric 3D Body Shape Estimation
- 3 Evaluation
- 4 Conclusion
- References
- Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery
- 1 Introduction
- 2 Multi-instrument Detector
- 2.1 Scene Model
- 2.2 Multi-instrument Detector Network
- 3 Experiments
- 4 Conclusion
- References
- A Comparative Study of Breast Surface Reconstruction for Aesthetic Outcome Assessment
- 1 Introduction
- 2 Methods
- 3 Experiments and Results
- 4 Conclusions
- References
- DejaVu: Intra-operative Simulation for Surgical Gesture Rehearsal
- 1 Introduction and Background
- 2 Materials and Methods
- 2.1 Overview of a DejaVu Simulation
- 2.2 Organ Physical Behavior and Dynamics
- 2.3 Organ Appearance and Scene Illumination
- 3 Results
- 4 Discussion and Conclusion
- References
- An Optimal Control Approach for High Intensity Focused Ultrasound Self-Scanning Treatment Planning
- 1 Introduction
- 2 Method
- 3 Materials and Results
- 4 Discussion
- 5 Conclusion
- References
- A Mixed-Reality Approach to Radiation-Free Training of C-arm Based Surgery
- 1 Introduction
- 2 Methodology
- 3 System Evaluation
- 4 Discussion
- References
- Image-Driven Stochastic Identification of Boundary Conditions for Predictive Simulation
- 1 Introduction
- 2 Method
- 2.1 Overview of the Algorithm
- 2.2 Stochastic Identification of Boundary Conditions
- 3 Results
- 3.1 Evaluation Using the Synthetic and Phantom Data
- 3.2 Evaluation Using In-vivo Data
- 4 Discussion and Conclusion
- References
- Toward Computer-Assisted Planning for Interstitial Laser Ablation of Malignant Brain Tumors Using a Tubular Continuum Robot
- 1 Introduction
- 2 Laser Ablation Using a Tubular Continuum Robot
- 3 Computer-Assisted Planning
- 4 Evaluation and Results
- References
- Providing Effective Real-Time Feedback in Simulation-Based Surgical Training
- 1 Introduction
- 2 Simulation Platform and Problem Definition
- 3 Random Forest Based Feedback Formulation
- 4 Experimental Evaluation
- 5 Conclusion
- References
- Interventional Imaging and Navigation
- Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy
- 1 Introduction
- 2 Method
- 2.1 Data
- 2.2 CNN Model
- 2.3 Training
- 2.4 Centerline Extraction
- 3 Experiments and Results
- 4 Discussion and Conclusion
- References
- Deep Neural Networks Predict Remaining Surgery Duration from Cholecystectomy Videos
- 1 Introduction
- 2 Methodology
- 2.1 Methods for RSD Prediction
- 2.2 Training Strategy
- 3 Experimental Setup
- 4 Experimental Results
- 5 Conclusions
- References
- UV Exposed Optical Fibers with Frequency Domain Reflectometry for Device Tracking in Intra-arterial Procedures
- 1 Introduction
- 2 Materials and Methods
- 2.1 Fabrication of UV Enhanced Optical Fibers
- 2.2 3D Shape Tracking Using OFDR
- 2.3 Roadmapping of Hepatic Arteries
- 2.4 Anisotropic Curvature Model Matching
- 3 Experiments and Results
- 3.1 Experimental Setup
- 3.2 Synthetic Vascular Models
- 3.3 Animal Experiment
- 4 Conclusion
- References
- Real-Time 3D Ultrasound Reconstruction and Visualization in the Context of Laparoscopy
- 1 Introduction
- 2 Methods
- 2.1 Calibration and Tracking
- 2.2 3D Freehand US Reconstruction
- 2.3 Visualizing US In-situ
- 3 Experiments
- 3.1 Setup
- 3.2 Experimental Task and Analysis
- 4 Results
- 5 Discussion and Future Work
- References
- Improving Needle Detection in 3D Ultrasound Using Orthogonal-Plane Convolutional Networks
- 1 Introduction
- 2 Methods
- 2.1 Needle-Voxel Classification
- 2.2 Needle-Axis Estimation and Visualization
- 3 Experimental Results
- 4 Discussions and Conclusions
- References
- Motion-Compensated Autonomous Scanning for Tumour Localisation Using Intraoperative Ultrasound
- 1 Introduction
- 2 Methods
- 2.1 Motion Estimation and Online Update
- 2.2 Motion-Compensated Scanning
- 2.3 Tumour Segmentation and Visualisation
- 3 Results
- 4 Conclusions
- References
- Deep Learning for Sensorless 3D Freehand Ultrasound Imaging
- 1 Introduction
- 2 Methods
- 2.1 From Speckle Decorrelation to Convolutional Neural Networks
- 2.2 Using Optical Flow as Additional Information
- 3 Experiments and Results
- 4 Conclusion
- References
- Ultrasonic Needle Tracking with a Fibre-Optic Ultrasound Transmitter for Guidance of Minimally Invasive Fetal Surgery
- 1 Introduction
- 2 Materials and Methods
- 2.1 Ultrasonic Tracking System
- 2.2 Image Processing
- 2.3 Tracking Accuracy and Signal-to-Noise Ratio Measurements
- 2.4 In Vivo Validation
- 3 Results and Discussion
- References
- Learning CNNs with Pairwise Domain Adaption for Real-Time 6DoF Ultrasound Transducer Detection and Tracking from X-Ray Images
- 1 Introduction
- 2 Method
- 2.1 System Overview
- 2.2 Hierarchical CNN Regression Architecture
- 2.3 Pairwise Domain Adaptation
- 2.4 A CNN Classifier to Resolve Pose Ambiguity
- 3 Experiment Setup, Results and Discussions
- 4 Conclusion
- References
- An Approach for Needle Based Optical Coherence Elastography Measurements
- 1 Introduction
- 2 Materials and Methods
- 2.1 Shear Wave Propagation
- 2.2 Optical Shear Wave Detection
- 2.3 Needle Probe Design
- 2.4 Experimental Setup and Calibration
- 2.5 Experimental Parameters
- 3 Results
- 4 Discussion
- 5 Conclusion
- References
- Concurrent Segmentation and Localization for Tracking of Surgical Instruments
- 1 Introduction and Related Work
- 2 Method
- 2.1 Encoder
- 2.2 Decoder Tasks
- 3 Experiments and Results
- 3.1 Evaluation of Modeling Strategies
- 3.2 Retinal Microsurgery
- 3.3 EndoVis Challenge
- 4 Conclusion
- References
- Surgical Soundtracks: Towards Automatic Musical Augmentation of Surgical Procedures
- 1 Introduction
- 2 Notations and Definitions
- 3 Related Work
- 4 Method
- 5 Experiments and Results
- 5.1 Discussion
- 6 Conclusion
- References
- Precise Ultrasound Bone Registration with Learning-Based Segmentation and Speed of Sound Calibration
- 1 Introduction
- 2 Methods
- 3 Experiments and Results
- 4 Conclusion
- References
- Convolutional Neural Network and In-Painting Techniques for the Automatic Assessment of Scoliotic Spine Surgery from Biplanar Radiographs
- Abstract
- 1 Introduction
- 2 Automatic 3D Spine Reconstruction
- 2.1 Statistical Spine Model
- 2.2 Vertebrae 3D/2D Registration Based on Convolutional Neural Networks
- 2.3 Postoperative Image In-Painting
- 3 Experiments
- 3.1 Vertebrae Location and Orientation
- 3.2 Clinical Parameters
- 4 Conclusions
- References
- Medical Image Computing
- A Novel Automatic Segmentation Method to Quantify the Effects of Spinal Cord Injury on Human Thigh Muscles and Adipose Tissue
- Abstract
- 1 Introduction
- 2 Methods
- 2.1 Joint Markov Gibbs Random Field Model
- 2.2 Appearance-Based Shape Model
- 2.3 Spatial Interaction or Second-Order Appearance Model
- 2.4 Intensity or First-Order Appearance Model
- 3 Experimental Results
- 4 Conclusions
- References
- OptiC: Robust and Automatic Spinal Cord Localization on a Large Variety of MRI Data Using a Distance Transform Based Global Optimization
- 1 Introduction
- 2 Method
- 2.1 Optimization Problem Resolution
- 2.2 Implementation Details
- 3 Experimental Results
- 3.1 Data Sets
- 3.2 Model Training
- 3.3 Performance Evaluation
- 4 Discussion and Conclusion
- References
- Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks
- 1 Introduction
- 2 Materials and Methods
- 3 Experiments
- 4 Discussion and Conclusion
- References
- Online Statistical Inference for Large-Scale Binary Images
- 1 Introduction
- 2 Probabilistic Model of Binary Segmentation
- 3 Online Algorithm for t-Test
- 4 Online Algorithm for Linear Regression
- 5 Online Algorithm for F-Test
- 6 Random Field Theory
- 7 Application
- 8 Discussion
- References
- Short Acquisition Time PET Quantification Using MRI-Based Pharmacokinetic Parameter Synthesis
- 1 Introduction
- 2 Methods
- 2.1 MRI-PET Database Construction and R1 Synthesis
- 2.2 PET Quantification Using the SRTM
- 2.3 CBF Estimation from ASL MRI
- 2.4 SRTM with Incomplete PET Scan and CBF
- 3 Experiments and Results
- 3.1 Regional Analysis of R1 and BPND Estimation
- 3.2 Voxel-Wise Analysis of R1 and BPND Estimation
- 4 Discussion and Conclusion
- References
- A Variational Approach to Sparse Model Error Estimation in Cardiac Electrophysiological Imaging
- 1 Introduction
- 2 Bayesian Formulation with Error Modeling
- 3 Posterior Regularized Bayes for EP Imaging
- 4 Results
- 5 Conclusion
- References
- Data-Driven Rank Aggregation with Application to Grand Challenges
- 1 Introduction
- 2 Methodology
- 2.1 Rank Aggregation
- 2.2 Assessing Stability with Perturbations
- 3 Experimental Validation
- 4 Conclusion
- References
- Benchmarking Quantitative Imaging Biomarker Measurement Methods Without a Gold Standard
- 1 Introduction
- 2 Framework Description
- 3 Validation
- 4 Discussion
- References
- Author Index
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