Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

Name: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 | 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I
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18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I

Nassir Navab Joachim Hornegger William M. Wells Alejandro Frangi(Editor)

Springer (Publisher)

Published on 28. September 2015

XLVII, 740 pages

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978-3-319-24553-9 (ISBN)

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Content

Intro
Preface
Organization
Contents - Part I
Part I Advanced MRI: Diffusion, fMRI, DCE
Automatic Segmentation of Renal Compartments in DCE-MRI Images
1 Introduction
2 Our 3-Step Method
2.1 Step 1 - Initial Segmentation Based on MSTV
2.2 Step 2 - PCA-kmeans Clustering for Renal Compartment Segmentation
2.3 Step 3 - Refinement
3 Experimental Results
4 Conclusion and Future Work
References
Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners
1 Introduction
2 Our Contributions
3 Method
3.1 Diffusion MRI and RISH Features
3.2 Mapping RISH Features Between Sites
4 Results
5 Conclusion and Limitations
References
Track Filtering via Iterative Correction of TDI Topology
1 Introduction
2 Methods
3 Test Subjects and Data Preparation
4 Results and Discussions
5 Conclusion
References
Novel Single and Multiple Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes
1 Introduction
2 Methods
2.1 Spherical Code (SC) Formulation:Maximize the Minimal Separation Angle
2.2 Constrained Non-Linear Optimization (CNLO)
2.3 Iterative Maximum Overlap Construction (IMOC)
3 Experiments
4 Conclusion
References
q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans
1 Introduction
2 Methods
3 Results
4 Discussion
References
A Machine Learning Based Approach to Fiber Tractography Using Classifier Voting
1 Introduction
2 Materials and Methods
2.1 Classifier Training
2.2 Streamline Propagation
2.3 Experiments
3 Results
4 Discussion and Conclusion
References
Symmetric Wiener Processes for Probabilistic Tractography and Connectivity Estimation
1 Introduction
2 Method
2.1 Angular Constraint and Implementation
3 Experiments
3.1 In-Vivo Human Brain
4 Discussion
References
An Iterated Complex Matrix Approach for Simulation and Analysis of Diffusion MRI Processes
1 Introduction
2 Theory and Method
2.1 The Short Time Limit, ?t 0
2.2 From Compartment Shape to Simulation Matrix
3 Results
3.1 Compartment Estimation
4 Conclusion and Discussion
References
Prediction of Motor Function in Very Preterm Infants Using Connectome Features and Local Synthetic Instances
1 Introduction
2 Methods
3 Results
4 Conclusions
References
Segmenting Kidney DCE-MRI Using 1st-Order Shape and 5th-Order Appearance Priors
1 Introduction
2 Shape-Appearance Guided Deformable Boundary
3 Experimental Validation and Conclusions
References
Comparison of Stochastic and Variational Solutions to ASL fMRI Data Analysis
1 Introduction
2 Joint Detection Estimation Model for fASL Data
3 Variational EM Estimation
4 Results
4.1 Artificial Data
4.2 Real Data
5 Conclusion
References
Prediction of CT Substitutes from MR Images Based on Local Sparse Correspondence Combination
1 Introduction
2 CT Prediction by LSCC
2.1 Basic Idea of LSCC
2.2 Local Dictionary Construction
2.3 Local Linear Representation
2.4 Prediction
3 Experimental Results
3.1 Performance of Using Multimodality MR Images
3.2 Comparison with the Relevant Methods
4 Conclusion
References
Robust Automated White Matter Pathway Reconstruction for Large Studies
1 Introduction
2 Method
3 Experiments
4 Results
5 Discussion and Conclusions
References
Exploiting the Phase in Diffusion MRI for Microstructure Recovery: Towards Axonal Tortuosity via Asymmetric Diffusion Processes
1 Introduction
2 Theory and Methods
2.1 Tortuous Axon Model
2.2 Ensemble Average Propagator for Tortuous Axons
2.3 Diffusion Signal and EAP Asymmetry
2.4 Monte-Carlo Simulation of DW-MRI in Tortuous Axons
3 Experiments
4 Discussion and Conclusion
References
Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI
1 Introduction
2 Methods
2.1 Dictionary Representation of High Resolution Data
2.2 Partial Volume Representation of Low Resolution Data
2.3 Bayesian Linear Un-mixing Inference
2.4 Priors
3 Experiments and Results
3.1 Simulated Data
3.2 In-Vivo MRI Data
4 Conclusions
References
Classification of MRI under the Presence of Disease Heterogeneity using Multi-Task Learning: Application to Bipolar Disorder
1 Introduction
2 Multi-Task l2,1 + l1-norm Support Vector Machine
2.1 Formulation
2.2 Solution
3 Results
3.1 Multi-Task Feature Learning on the Simulated Data
3.2 Multi-Task Classification on the Bipolar Disorder Data
4 Conclusions
References
Fiber Connection Pattern-Guided Structured Sparse Representation of Whole-Brain fMRI Signals for Functional Network Inference
1 Introduction
2 Materials and Methods
2.1 Data Acquisition and Pre-processing
2.2 Dictionary Learning of Whole-Brain rsfMRI Signals
2.3 Fiber Connection Pattern Based Cortical Parcellation for Constraint
2.4 Structured Sparse Representation of Whole-Brain rsfMRI Signals
3 Experimental Results
3.1 Comparison of Identified Functional Networks
3.2 Functional Networks Guided by Multiple Levels of Cortical Parcellation
3.3 Co-visualization of Other Identified Functional Networks
4 Discussion and Conclusion
References
Joint Estimation of Hemodynamic Response Function and Voxel Activation in Functional MRI Data
1 Introduction
2 Preliminaries
2.1 Cerebral Hemodynamic Response Function
2.2 Empirical Mode Decomposition
3 Proposed Estimation of HRF and Voxel Activation
3.1 Proposed HRF Estimation Method
3.2 Proposed Voxel Activation Detection via Subspace Modeling
4 Validation of the Proposed Method
4.1 Results on Synthetic fMRI Data
4.2 Results on Real fMRI Data
5 Conclusion
References
Quantifying Microstructure in Fiber Crossings with Diffusional Kurtosis
1 Introduction
2 Related Work
3 How Fiber Crossings Affect Diffusional Kurtosis
4 A Mixture of Kurtosis Models
4.1 A Cylindrically Symmetric Kurtosis Model
4.2 Strategy for Fitting the Final Mixture
5 Results
5.1 Simulated Data
5.2 Real Data
6 Conclusion
References
Which Manifold Should be Used for Group Comparison in Diffusion Tensor Imaging?
1 Introduction
2 Methods
2.1 Pre-processing
2.2 Multi-linear Regression
2.3 Statistical Test
3 Validation Framework
3.1 Synthetic Data
3.2 Neuromyelitis Optica Cohort
4 Results
4.1 Results on Synthetic Data
4.2 Results on NMO Patients
5 Conclusion and Discussion
References
Convex Non-negative Spherical Factorization of Multi-Shell Diffusion-Weighted Images
1 Introduction
2 Method
3 Experiments and Results
3.1 Monte-Carlo Simulations
3.2 Results on Human Brain Data
4 Discussion and Conclusion
References
Tensorial Spherical Polar Fourier Diffusion MRI with Optimal Dictionary Learning
1 Introduction
2 Tensorial Spherical Polar Fourier Imaging (TSPFI)
3 TSPFI with Optimal Dictionary Learning (DL-TSPFI)
4 Experiments
5 Conclusion
References
Diffusion Compartmentalization Using Response Function Groups with Cardinality Penalization
1 Introduction
2 Proposed Approach
2.1 Response Function Groups (RFGs)
2.2 Estimation of Volume Fractions
2.3 Optimization
3 Experimental Results
3.1 Data
3.2 Diffusion Parameters
3.3 Comparison Methods
3.4 Results
4 Conclusion
References
V-Bundles: Clustering Fiber Trajectories from Diffusion MRI in Linear Time
1 Introduction
2 Methods and Material
2.1 Vector Field k-Means
2.2 V-Bundles
2.3 Material
3 Results
4 Discussion and Conclusion
References
Assessment of Mean Apparent Propagator-Based Indices as Biomarkers of Axonal Remodeling after Stroke
1 Introduction
2 Materials and Methods
2.1 Analytical Model for Signal Reconstruction
2.2 Tract-Based Quantitative Analysis
2.3 Statistical Analysis
3 Results and Discussions
4 Conclusions
References
Integrating Multimodal Priors in Predictive Models for the Functional Characterization of Alzheimer's Disease
1 Introduction
2 Multimodal Prior Integration for Model Enhancement
3 Experiments
4 Results and Discussion
5 Conclusion
References
Simultaneous Denoising and Registration for Accurate Cardiac Diffusion Tensor Reconstruction from MRI
1 Introduction
2 Methods
2.1 Denoising
2.2 Registration-Guided (RG) Denoising
2.3 Simultaneous Denoising and Registration
3 Results
4 Conclusions
References
Iterative Subspace Screening for Rapid Sparse Estimation of Brain Tissue Microstructural Properties
1 Introduction
2 Approach
2.1 Problem Description
2.2 Iterative Subspace Screening (ISS)
3 Experiments
3.1 Data
3.2 Methods of Evaluation
3.3 Results
4 Conclusion
References
Elucidating Intravoxel Geometry in Diffusion-MRI: Asymmetric Orientation Distribution Functions (AODFs) Revealed by a Cone Model
1 Introduction
2 Method
2.1 Asymmetric ODFs Based on a Cone Model
2.2 Measuring Asymmetry
3 Experimental Results
3.1 Synthetic Bending and Crossing Fibers
3.2 Results on Real Diffusion MRI Data
3.3 Asymmetry Index Map Results
4 Discussion and Conclusion
References
Modeling Task FMRI Data via Supervised Stochastic Coordinate Coding
1 Introduction
2 Methods
2.1 Stochastic Coordinate Coding of FMRI Data
2.2 Fix Temporal Features in Stochastic Coordinate Coding
2.3 Constrain Spatial Maps in Stochastic Coordinate Coding
2.4 Group-Wise Statistical Aanlysis of Network Spatial Maps
3 Experimental Results
3.1 Detecting Task-Evoked Networks Using Supervised SCC
3.2 Networks Detected By Restriction of Spatial Maps
3.3 Automatically Learned Concurrent Networks
4 Conclusion
References
Computer Assisted and Image-Guided Interventions
Autonomous Ultrasound-Guided Tissue Dissection
1 Introduction
2 Materials and Methods
2.1 Experimental Setup
2.2 System Architecture
2.3 Robust Jacobian Estimation
2.4 Coordinate Frame Relationships
2.5 Visual Servoing with Multimodal Inputs
2.6 Dissection Path Generation
3 Results
3.1 Jacobian Estimation
3.2 Tissue Dissection
4 Conclusion
References
A Compact Retinal-Surgery Telemanipulator that Uses Disposable Instruments
1 Introduction
2 System Design
3 Membrane-Peeling Experiments and Results
4 Discussion
References
Surgical Tool Tracking and Pose Estimation in Retinal Microsurgery
1 Introduction
2 Proposed Method
2.1 Regression Forest
2.2 Tracking
2.3 Pose Estimation
3 Experiments and Results
3.1 Public Dataset Evaluation
3.2 Appearance Dataset Evaluation
4 Conclusions
References
Direct Calibration of a Laser Ablation System in the Projective Voltage Space
1 Introduction
2 Methods
2.1 Tilting Mirror Calibration with the DLT
2.2 Calibration Errors
2.3 Acquiring 2D-3D Correspondences
2.4 Integration of the Tilting Mirror
3 Experiments and Results
4 Conclusion
References
Surrogate-Driven Estimation of Respiratory Motion and Layers in X-Ray Fluoroscopy
1 Introduction
2 Methods
2.1 Image Formation Model
2.2 Surrogate-Driven Motion Model
2.3 Motion and Layer Estimation
2.4 Implementation
3 Experiments and Results
3.1 Simulated Data
3.2 Clinical Data
4 Conclusion and Outlook
References
Robust 5DOF Transesophageal Echo Probe Tracking at Fluoroscopic Frame Rates
1 Introduction
2 Methods
2.1 Algorithm
2.2 Experiments
3 Results
4 Discussion
5 Conclusion
References
Rigid Motion Compensation in Interventional C-arm CT Using Consistency Measure on Projection Data
1 Introduction
2 Methods
2.1 Objective Function
2.2 Optimization Procedure
2.3 Implementation
3 Results
4 Conclusions
References
Hough Forests for Real-Time, Automatic Device Localization in Fluoroscopic Images: Application to TAVR
1 Introduction
2 Methods
2.1 Algorithm
2.2 Experiments
3 Results
4 Discussion
5 Conclusion
References
Hybrid Utrasound and MRI Acquisitions for High-Speed Imaging of Respiratory Organ Motion
1 Introduction
2 Materials and Methods
2.1 Hardware Setup and Data Acquisition
2.2 Algorithm - Simpler Case: Single-Plane MR Acquisition
2.3 Algorithm - Extension to Multiple-Plane MR Acquisition
3 Results and Discussion
References
A Portable Intra-Operative Framework Applied to Distal Radius Fracture Surgery
1 Introduction
2 Methods
2.1 Intra-Operative Planning
2.2 Intra-Operative Guidance
2.3 Instrument Visualization
3 Experiments
4 Discussion and Conclusions
References
Image Based Surgical Instrument Pose Estimation with Multi-class Labelling and Optical Flow
1 Introduction
2 Method
2.1 Multi-label Probabilistic Classification
2.2 Multi-region Segmentation with Level Sets
2.3 Optimization and Tracking
3 Results
3.1 Quantitative Validation
3.2 Qualitative Validation
4 Conclusion and Discussion
References
Adaption of 3D Models to 2D X-Ray Images during Endovascular Abdominal Aneurysm Repair
1 Introduction
2 Methods
2.1 Skeleton-Based As-Rigid-As-Possible Mesh Deformation
2.2 Control Point Selection
2.3 Mesh Deformation
3 Results and Evaluation
3.1 Data Description
3.2 Qualitative Results
3.3 Quantitative Results
4 Conclusion and Outlook
References
Projection-Based Phase Features for Localization of a Needle Tip in 2D Curvilinear Ultrasound
1 Introduction
2 Methods
2.1 Needle Tip Localization Using Projection-Based Image Phase Features
2.2 Data Acquisition and Experiments
3 Results
4 Discussions and Conclusions
References
Inertial Measurement Unit for Radiation-Free Navigated Screw Placement in Slipped Capital Femoral Epiphysis Surgery
1 Introduction
2 Methodology
3 Results
4 Conclusion and Future Work
References
Pictorial Structures on RGB-D Images for Human Pose Estimation in the Operating Room
1 Introduction
2 Method
2.1 Flexible Mixtures of Parts
2.2 Appearance Model
3 Experimental Results and Discussions
4 Conclusions
References
Interventional Photoacoustic Imaging of the Human Placenta with Ultrasonic Tracking for Minimally Invasive Fetal Surgeries
1 Introduction
2 Materials and Methods
2.1 PA Imaging and Ultrasonic Tracking System
2.2 Imaging and Tracking with a Human Placenta
3 Results and Discussion
References
Automated Segmentation of Surgical Motion for Performance Analysis and Feedback
1 Introduction
2 Method
3 Experiment Results
4 Discussion and Conclusion
References
Vision-Based Intraoperative Cone-Beam CT Stitching for Non-overlapping Volumes
1 Introduction
2 Materials and Methods
2.1 System Setup and Calibration
2.2 CBCT Volume and Video Acquisition
2.3 Two-Dimensional Feature Detection and Matching
2.4 Recovering Three-Dimensional Coordinates
2.5 Estimating 3D Transformation and CBCT Volume Stitching
3 Experiments and Results
4 Discussion and Conclusion
References
Visibility Map: A New Method in Evaluation Quality of Optical Colonoscopy
1 Introduction
2 Method
2.1 Camera Motion Estimation
2.2 Generating Visibility Map
3 Experiments and Results
3.1 Simulated Video
3.2 Extracted Visibility Map from Simulated Videos
3.3 Application to Actual Colonoscopy Video
4 Discussion and Conclusion
References
Tissue Surface Reconstruction Aided by Local Normal Information Using a Self-calibrated Endoscopic Structured Light System
1 Introduction
2 Methods
3 Experiments and Results
4 Discussion and Conclusion
References
Surgical Augmented Reality with Topological Changes
1 Introduction
2 Method
2.1 Coupling Real and Virtual Organs
2.2 Detecting Discontinuities in Motion
2.3 Robust Processing of the Cut
3 Experimental Results
4 Conclusion and Discussion
References
Towards an Efficient Computational Framework for Guiding Surgical Resection through Intra-operative Endo-microscopic Pathology
1 Introduction
2 Computational Pipeline
3 Experiments
4 Results and Discussions
5 Conclusion
References
Automated Assessment of Surgical Skills Using Frequency Analysis
1 Introduction
2 Background
3 Framework for Skill Assessment
4 Experimental Evaluation
5 Results and Discussion
References
Robust Live Tracking of Mitral Valve Annulus for Minimally-Invasive Intervention Guidance
1 Introduction
2 Methods
2.1 Mitral Valve Annulus Detector
2.2 Optical Flow Key Frame Tracker
3 Experiments and Results
3.1 Dataset
3.2 Quantitative Evaluation
4 Conclusion
References
Motion-Aware Mosaicing for Confocal Laser Endomicroscopy
Introduction
1 Dynamic Mosaicing
1.1 Problem Statement and Related Work
1.2 Markov Random Field Formulation
1.3 Hierachical Optimization
1.4 Gradient-Domain Composition
1.5 Looping Mosaic
2 Combining Static and Dynamic Mosaics
2.1 Texture Preserving Static Mosaicing
2.2 Static Background Detection
2.3 Combining Still Images and Video Segments
3 Experiments and Results
3.1 Materials
3.2 Consistency of the Visual Summary
4 Discussion
References
A Registration Approach to Endoscopic Laser Speckle Contrast Imaging for Intrauterine Visualisation of Placental Vessels
1 Introduction
2 Methods
3 Experiments and Results
4 Discussion
References
Marker-Less AR in the Hybrid Room Using Equipment Detection for Camera Relocalization
1 Introduction
2 Methods
2.1 System Setup
2.2 Tracking Pipeline
2.3 Equipment Detection
3 Results
4 Conclusion
References
Hybrid Retargeting for High-Speed Targeted Optical Biopsies
1 Introduction
2 Methods
3 Results
4 Conclusion
References
Visual Force Feedback for Hand-Held Microsurgical Instruments
1 Introduction
2 Materials and Methods
2.1 System Overview
2.2 Visual Force-Feedback
2.3 Augmented Reality Integration
2.4 Force-Based Displacement
3 Experimental Setup and Results
3.1 Retraction Study
3.2 Dissection Study
3.3 Depth Perception Study
4 Discussion and Conclusion
References
Automated Three-Piece Digital Dental Articulation
1 Introduction
2 Algorithm for Automatic 3-Piece Dental Articulation
2.1 Anterior Segment Alignment
2.2 Right and Left Posterior Segments Alignment
3 Experiments and Results
3.1 Dental Models and Digital Dental Articulation
3.2 Qualitative Validation and Results
3.3 Quantitative Validation
4 Discussion and Future Work
References
A System for MR-Ultrasound Guidance during Robot-Assisted Laparoscopic Radical Prostatectomy
1 Introduction
2 Materials and Methods
2.1 TRUS Imaging System
2.2 TRUS-MR Registration
2.3 in-vivo Patient Studies and Results
3 Discussions and Conclusions
References
Computer Aided Diagnosis I: Machine Learning
Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks
1 Introduction
2 Method
2.1 Joint Learning with Knowledge Transfer across Multi-tasks
2.2 US Standard Plane Detection via T-RNN
3 Experiments and Results
4 Conclusion
References
Automatic Localization and Identification of Vertebrae in Spine CT via a Joint Learning Model with Deep Neural Networks
1 Introduction
2 Method
2.1 Coarse Vertebra Candidates Localization
2.2 J-CNN for Vertebrae Identification
2.3 Localization Refinement with Shape Regression Modelling
3 Experiments
4 Discussion and Conclusion
References
Identification of Cerebral Small Vessel Disease Using Multiple Instance Learning
1 Introduction
2 Methods
3 Patch Extraction
4 Experiments and Results
4.1 Imaging Data and Pre-processing
4.2 Patch-Based Identification of SVD
5 Discussion and Conclusion
References
Why Does Synthesized Data Improve Multi-sequence Classification?
1 Introduction
2 Methods
3 Data and Implementation
4 Experiments
5 Results
6 Discussion and Conclusion
References
Label Stability in Multiple Instance Learning
1 Introduction
2 Multiple Instance Learning
3 Instance Stability
4 Experiments and Results
5 Conclusions
References
Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation
1 Introduction
2 Method
3 Results
3.1 Euclidean versus Spectral Coordinates
3.2 Full Cortical Parcellation
4 Conclusion
References
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
1 Introduction
2 Methods
2.1 Candidate Region Generation
2.2 Convolutional Neural Network (ConvNet) Setup
2.3 P-ConvNet: Deep Patch Classification
2.4 R-ConvNet: Deep Region Classification
2.5 Data Augmentation
2.6 Cross-Scale and 3D Probability Aggregation
3 Results and Discussion
4 Conclusion
References
3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data
1 Introduction
2 Efficient Detection with Neural Networks
3 Robust Detection by Combining Multiple Features
4 Experiments
5 Conclusions
References
A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI
1 Introduction
2 Materials and Preprocessing
3 Proposed Method
3.1 Deep Auto-Encoder
3.2 Hidden Markov Models
4 Experiments and Discussion
4.1 Experimental Settings
4.2 Performance Comparison
5 Conclusion
References
Combining Unsupervised Feature Learning and Riesz Wavelets for Histopathology Image Representation: Application to Identifying Anaplastic Medulloblastoma
1 Introduction
2 Methodological Description
2.1 Topographic Independent Component Analysis
2.2 Image Representation via Rotation-Covariant Riesz Wavelets
2.3 Fusing UFL and Riesz Features
3 Experimental Results and Discussion
3.1 Medulloblastoma Dataset
3.2 Experimental Setup
3.3 Results
4 Concluding Remarks
References
Automatic Coronary Calcium Scoring in Cardiac CT Angiography Using Convolutional Neural Networks
1 Introduction
2 Material and Methods
2.1 Data
2.2 Automatic CAC Scoring
3 Experiments and Results
4 Discussion and Conclusion
References
A Random Riemannian Metric for Probabilistic Shortest-Path Tractography
1 Introduction
2 Shortest-Path Tractography and Random Geometries
3 Regressing an ODE
4 Experimental Results
5 Discussion and Conclusion
References
Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms
1 Introduction
2 Methodology
2.1 Conditional Random Field (CRF)
2.2 Structured Support Vector Machine (SSVM)
2.3 Potential Functions
3 Experiments
3.1 Materials and Methods
3.2 Results
4 Discussion and Conclusions
References
Learning Tensor-Based Features for Whole-Brain fMRI Classification
1 Introduction
2 Methods
3 Experiments and Discussions
4 Conclusion
References
Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography Using Supervised Learning and a Novel Morphometric Feature Descriptor
1 Introduction
2 Methods
2.1 Feature Extraction
2.2 Multi-output Regression Model
3 Results and Discussion
3.1 Datasource
3.2 Evaluation Metric
3.3 Experiment
4 Conclusion and Future Work
References
Automatic Diagnosis of Ovarian Carcinomas via Sparse Multiresolution Tissue Representation
1 Introduction
2 Approach
3 Experiments and Discussion
4 Conclusion
References
Scale-Adaptive Forest Training via an Efficient Feature Sampling Scheme
1 Introduction
2 Methods
2.1 Haar-Like Features for Segmentation
2.2 Classification Forests
2.3 Fine-to-Coarse Sequential Feature Sampling
3 Experiments
4 Conclusion
References
Multiple Instance Cancer Detection by Boosting Regularised Trees
1 Introduction
2 Related Work
3 Method
3.1 Notation
3.2 Discriminative Prototypes
3.3 Boosting with Regularised Regression Trees
4 Evaluations
4.1 Breast Cancer TMA Images
4.2 OPT Images of Colorectal Polyps
5 Conclusions
References
Uncertainty-Driven Forest Predictors for Vertebra Localization and Segmentation
1 Introduction
2 Method
3 Experiments
4 Results and Discussion
5 Conclusion
References
Who Is Talking to Whom: Synaptic Partner Detection in Anisotropic Volumes of Insect Brain
1 Introduction
2 Methods
2.1 Unary Factors
2.2 Pairwise Factors
3 Results
4 Discussion
References
Direct and Simultaneous Four-Chamber Volume Estimation by Multi-Output Regression
1 Introduction
2 Cardiac Four-Chamber Volume Estimation via Multi-Output Regression
2.1 Cardiac Image Representations
2.2 Multi-Output Regression with Random Forests
3 Experiments
3.1 Dataset and Implementation Details
3.2 Simultaneous Four-Chamber Volume Estimation
4 Conclusion
References
Cross-Domain Synthesis of Medical Images Using Efficient Location-Sensitive Deep Network
1 Introduction
2 Location-Sensitive Deep Network
2.1 Training LSDN
3 Network Simplification
4 Experiments
5 Conclusions
References
Grouping Total Variation and Sparsity: Statistical Learning with Segmenting Penalties
1 Introduction
2 Sparse Variation: A New Spatially Regularizing Penalty
2.1 Penalized Regression: Problem Formulation and Prior Art
2.2 A New Penalty for Segmentation Purposes: Sparse Variation
2.3 Optimization Strategy
3 Empirical Results
3.1 A Simple 1D Signal Recovery Problem
3.2 Segmenting Regions from MRI Data
3.3 Optimization Speed of FASTAA
References
Scandent Tree: A Random Forest Learning Method for Incomplete Multimodal Datasets
1 Introduction
2 Method
3 Results and Discussion
4 Conclusion
References
Disentangling Disease Heterogeneity with Max-Margin Multiple Hyperplane Classifier
1 Introduction
2 Method
2.1 Margin for Multiple Hyperplanes - Polytope
3 Experimental Validation
4 Conclusion
References
Marginal Space Deep Learning: Efficient Architecture for Detection in Volumetric Image Data
1 Introduction
2 Related Work
3 Method
3.1 Sparse Deep Learning Architectures
3.2 Marginal Space Deep Learning
4 Experimental Results
5 Conclusion
References
Nonlinear Regression on Riemannian Manifolds and Its Applications to Neuro-Image Analysis
1 Introduction
2 Methodology
2.1 Manifold Valued Independent Variable
2.2 Manifold Valued Dependent Variable
3 Experimental Results
3.1 Manifold Valued Independent Variable
3.2 Manifold Valued Dependent Variable
4 Conclusions
References
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Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

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