James S. Duncan

Boundary finding with parametrically deformable models

Segmentation using boundary finding is enhanced both by considering the boundary as a whole and by using model-based global shape information. The authors apply flexible constraints, in the form of a probabilistic deformable model, to the problem of segmenting natural 2-D objects whose diversity and irregularity of shape make them poorly represented in terms of fixed features or form. The parametric model is based on the elliptic Fourier decomposition of the boundary. Probability distributions on the parameters of the representation bias the model to a particular overall shape while allowing for deformations. Boundary finding is formulated as an optimization problem using a maximum a posteriori objective function. Results of the method applied to real and synthetic images are presented, including an evaluation of the dependence of the method on prior information and image quality. >

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005

Image Analysis and Validation.- Classification of Structural Images via High-Dimensional Image Warping, Robust Feature Extraction, and SVM.- Bone Enhancement Filtering: Application to Sinus Bone Segmentation and Simulation of Pituitary Surgery.- Simultaneous Registration and Segmentation of Anatomical Structures from Brain MRI.- Synthetic Ground Truth for Validation of Brain Tumor MRI Segmentation.- Vascular Image Segmentation.- Automatic Cerebrovascular Segmentation by Accurate Probabilistic Modeling of TOF-MRA Images.- A Segmentation and Reconstruction Technique for 3D Vascular Structures.- MRA Image Segmentation with Capillary Active Contour.- Spatial Graphs for Intra-cranial Vascular Network Characterization, Generation, and Discrimination.- Image Registration I.- Surface Alignment of 3D Spherical Harmonic Models: Application to Cardiac MRI Analysis.- Unified Point Selection and Surface-Based Registration Using a Particle Filter.- Elastic Registration of 3D Ultrasound Images.- Tracer Kinetic Model-Driven Registration for Dynamic Contrast Enhanced MRI Time Series.- Generalised Overlap Measures for Assessment of Pairwise and Groupwise Image Registration and Segmentation.- Diffusion Tensor Image Analysis.- Uncertainty in White Matter Fiber Tractography.- Fast and Simple Calculus on Tensors in the Log-Euclidean Framework.- 3D Curve Inference for Diffusion MRI Regularization.- Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis.- White Matter Tract Clustering and Correspondence in Populations.- 76-Space Analysis of Grey Matter Diffusivity: Methods and Applications.- Fast Orientation Mapping from HARDI.- An Automated Approach to Connectivity-Based Partitioning of Brain Structures.- Deformable Registration of Diffusion Tensor MR Images with Explicit Orientation Optimization.- A Hamilton-Jacobi-Bellman Approach to High Angular Resolution Diffusion Tractography.- Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI.- MR Diffusion-Based Inference of a Fiber Bundle Model from a Population of Subjects.- Knowledge-Based Classification of Neuronal Fibers in Entire Brain.- A Physical Model for DT-MRI Based Connectivity Map Computation.- Image Segmentation and Analysis I.- A Novel 3D Partitioned Active Shape Model for Segmentation of Brain MR Images.- Cross Entropy: A New Solver for Markov Random Field Modeling and Applications to Medical Image Segmentation.- Semi-automated Basal Ganglia Segmentation Using Large Deformation Diffeomorphic Metric Mapping.- Particle Filters, a Quasi-Monte Carlo Solution for Segmentation of Coronaries.- Hybrid Segmentation Framework for Tissue Images Containing Gene Expression Data.- Fully Automatic Kidneys Detection in 2D CT Images: A Statistical Approach.- Segmentation of Neighboring Organs in Medical Image with Model Competition.- Point-Based Geometric Deformable Models for Medical Image Segmentation.- A Variational PDE Based Level Set Method for a Simultaneous Segmentation and Non-rigid Registration.- A Tracking Approach to Parcellation of the Cerebral Cortex.- Cell Segmentation, Tracking, and Mitosis Detection Using Temporal Context.- A Unifying Approach to Registration, Segmentation, and Intensity Correction.- Automatic 3D Segmentation of Intravascular Ultrasound Images Using Region and Contour Information.- Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme.- Automatic Segmentation of the Left Ventricle in 3D SPECT Data by Registration with a Dynamic Anatomic Model.- Intravascular Ultrasound-Based Imaging of Vasa Vasorum for the Detection of Vulnerable Atherosclerotic Plaque.- Parametric Response Surface Models for Analysis of Multi-site fMRI Data.- Clinical Applications - Validation.- Subject Specific Finite Element Modelling of the Levator Ani.- Robust Visualization of the Dental Occlusion by a Double Scan Procedure.- Segmentation of Focal Cortical Dysplasia Lesions Using a Feature-Based Level Set.- Effects of Healthy Aging Measured By Intracranial Compartment Volumes Using a Designed MR Brain Database.- Predicting Clinical Variable from MRI Features: Application to MMSE in MCI.- Finite Element Modeling of Brain Tumor Mass-Effect from 3D Medical Images.- STREM: A Robust Multidimensional Parametric Method to Segment MS Lesions in MRI.- Cross Validation of Experts Versus Registration Methods for Target Localization in Deep Brain Stimulation.- Localization of Abnormal Conduction Pathways for Tachyarrhythmia Treatment Using Tagged MRI.- Automatic Mammary Duct Detection in 3D Ultrasound.- Automatic Segmentation of Intra-treatment CT Images for Adaptive Radiation Therapy of the Prostate.- Inter-Operator Variability in Perfusion Assessment of Tumors in MRI Using Automated AIF Detection.- Computer-Assisted Deformity Correction Using the Ilizarov Method.- Real-Time Interactive Viewing of 4D Kinematic MR Joint Studies.- Computer-Assisted Ankle Joint Arthroplasty Using Bio-engineered Autografts.- Prospective Head Motion Compensation for MRI by Updating the Gradients and Radio Frequency During Data Acquisition.- Harmonic Skeleton Guided Evaluation of Stenoses in Human Coronary Arteries.- Acquisition-Related Limitations in MRI Based Morphometry.- Combining Classifiers Using Their Receiver Operating Characteristics and Maximum Likelihood Estimation.- Two Methods for Validating Brain Tissue Classifiers.- Comparison of Vessel Segmentations Using STAPLE.- Validation Framework of the Finite Element Modeling of Liver Tissue.- A Complete Augmented Reality Guidance System for Liver Punctures: First Clinical Evaluation.- Imaging Systems - Visualization.- A Novel Approach to High Resolution Fetal Brain MR Imaging.- Respiratory Signal Extraction for 4D CT Imaging of the Thorax from Cone-Beam CT Projections.- Registering Liver Pathological Images with Prior In Vivo CT/MRI Data.- Support Vector Clustering for Brain Activation Detection.- Inter-frame Motion Correction for MR Thermometry.- Adaptive Multiscale Ultrasound Compounding Using Phase Information.- 3D Freehand Ultrasound Reconstruction Based on Probe Trajectory.- Self-Calibrating Ultrasound-to-CT Bone Registration.- A Hand-Held Probe for Vibro-Elastography.- Real-Time Quality Control of Tracked Ultrasound.- Fully Truncated Cone-Beam Reconstruction on Pi Lines Using Prior CT.- C-arm Calibration - Is it Really Necessary?.- Laser Needle Guide for the Sonic Flashlight.- Differential Fly-Throughs (DFT): A General Framework for Computing Flight Paths.- Panoramic Views for Virtual Endoscopy.- Computer Assisted Diagnosis.- Toward Automatic Computer Aided Dental X-ray Analysis Using Level Set Method.- Exploiting Temporal Information in Functional Magnetic Resonance Imaging Brain Data.- Model-Based Analysis of Local Shape for Lesion Detection in CT Scans.- Development of a Navigation-Based CAD System for Colon.- A Prediction Framework for Cardiac Resynchronization Therapy Via 4D Cardiac Motion Analysis.- Segmentation and Size Measurement of Polyps in CT Colonography.- Quantitative Nodule Detection in Low Dose Chest CT Scans: New Template Modeling and Evaluation for CAD System Design.- Graph Embedding to Improve Supervised Classification and Novel Class Detection: Application to Prostate Cancer.- Quantification of Emphysema Severity by Histogram Analysis of CT Scans.- Cellular and Molecular Image Analysis.- Efficient Learning by Combining Confidence-Rated Classifiers to Incorporate Unlabeled Medical Data.- Mosaicing of Confocal Microscopic In Vivo Soft Tissue Video Sequences.- Segmentation and 3D Reconstruction of Microtubules in Total Internal Reflection Fluorescence Microscopy (TIRFM).- Physically-Based Modeling.- Ligament Strains Predict Knee Motion After Total Joint Replacement.- A Boundary Element-Based Approach to Analysis of LV Deformation.- Reconstruction of Cerebrospinal Fluid Flow in the Third Ventricle Based on MRI Data.- Schwarz Meets Schwann: Design and Fabrication of Biomorphic Tissue Engineering Scaffolds.- Robotics and Intervention I.- Automatic Detection and Segmentation of Robot-Assisted Surgical Motions.- DaVinci Canvas: A Telerobotic Surgical System with Integrated, Robot-Assisted, Laparoscopic Ultrasound Capability.- Design and Control of In-Vivo Magnetic Microrobots.- 3D Needle-Tissue Interaction Simulation for Prostate Brachytherapy.- Development and Application of Functional Databases for Planning Deep-Brain Neurosurgical Procedures.- Medical Image Computing for Clinical Applications.- Gaze-Contingent Soft Tissue Deformation Tracking for Minimally Invasive Robotic Surgery.- Registration and Integration for Fluoroscopy Device Enhancement.- Computer Aided Detection for Low-Dose CT Colonography.- Photo-Realistic Tissue Reflectance Modelling for Minimally Invasive Surgical Simulation.- Biological Imaging - Simulation and Modeling I.- Motion Tracking and Intensity Surface Recovery in Microscopic Nuclear Images.- Towards Automated Cellular Image Segmentation for RNAi Genome-Wide Screening.- Adaptive Spatio-Temporal Restoration for 4D Fluorescence Microscopic Imaging.- Kinematic Geometry of Osteotomies.- Predictive Camera Tracking for Bronchoscope Simulation with CONDensation.- Experimental Validation of a 3D Dynamic Finite-Element Model of a Total Knee Replacement.- An In Vitro Patient-Tailored Model of Human Cerebral Artery for Simulating Endovascular Intervention.

Medical Image Databases: A Content-based Retrieval Approach

Information contained in medical images differs considerably from that residing in alphanumeric format. The difference can be attributed to four characteristics: (1) the semantics of medical knowledge extractable from images is imprecise; (2) image information contains form and spatial data, which are not expressible in conventional language; (3) a large part of image information is geometric; (4) diagnostic inferences derived from images rest on an incomplete, continuously evolving model of normality. This paper explores the differentiating characteristics of text versus images and their impact on design of a medical image database intended to allow content-based indexing and retrieval. One strategy for implementing medical image databases is presented, which employs object-oriented iconic queries, semantics by association with prototypes, and a generic schema.

Model-based deformable surface finding for medical images

Describes a new global shape parameterization for smoothly deformable three-dimensional (3-D) objects, such as those found in biomedical images, whose diversity and irregularity make them difficult to represent in terms of fixed features or parts. This representation is used for geometric surface matching to 3-D medical image data, such as from magnetic resonance imaging (MRI). The parameterization decomposes the surface into sinusoidal basis functions. Four types of surfaces are modeled: tori, open surfaces, closed surfaces and tubes. This parameterization allows a wide variety of smooth surfaces to be described with a small number of parameters. Extrinsic model-based information is incorporated by introducing prior probabilities on the parameters. Surface finding is formulated as an optimization problem. Results of the method applied to synthetic images and 3-D medical images of the heart and brain are presented.

Estimation of 3-D left ventricular deformation from medical images using biomechanical models

The quantitative estimation of regional cardiac deformation from three-dimensional (3-D) image sequences has important clinical implications for the assessment of viability in the heart wall. We present here a generic methodology for estimating soft tissue deformation which integrates image-derived information with biomechanical models, and apply it to the problem of cardiac deformation estimation. The method is image modality independent. The images are segmented interactively and then initial correspondence is established using a shape-tracking approach. A dense motion field is then estimated using a transversely isotropic, linear-elastic model, which accounts for the muscle fiber directions in the left ventricle. The dense motion field is in turn used to calculate the deformation of the heart wall in terms of strain in cardiac specific directions. The strains obtained using this approach in open-chest dogs before and after coronary occlusion, exhibit a high correlation with strains produced in the same animals using implanted markers. Further, they show good agreement with previously published results in the literature. This proposed method provides quantitative regional 3-D estimates of heart deformation.

A robust point-matching algorithm for autoradiograph alignment.

We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the one-to-one correspondences (or homologies) between point features extracted from the images and rejecting non-homologies as outliers. In this way, we attempt to account for the local, natural and artifactual differences between the autoradiograph slices. We have used the resulting automated algorithm on a set of left prefrontal cortex autoradiograph slices, specifically demonstrated its ability to perform point outlier rejection, validated its robustness property using synthetically generated spatial mappings and provided an anecdotal visual comparison with the well-known iterated closest-point (ICP) algorithm. Visualization of a stack of aligned left prefrontal cortex autoradiograph slices is also provided.

Model-driven brain shift compensation

Surgical navigation systems provide the surgeon with a display of preoperative and intraoperative data in the same coordinate system. However, the systems currently in use in neurosurgery are subject to inaccuracy caused by intraoperative brain deformation (brain shift), since they typically assume that the intracranial structures are rigid. Experiments show brain shift of up to 1 cm, making it the dominant error in the system. We propose a biomechanical-model-based approach for brain shift compensation. Two models are presented: a damped spring-mass model and a model based on continuum mechanics. Both models are guided by limited intraoperative (exposed brain) surface data, with the aim to recover the deformation in the full volume. The two models are compared and their advantages and disadvantages discussed. A partial validation using intraoperative MR image sequences indicates that the approach reduces the error caused by brain shift.

Neighbor-constrained segmentation with level set based 3-D deformable models

A novel method for the segmentation of multiple objects from three-dimensional (3-D) medical images using interobject constraints is presented. Our method is motivated by the observation that neighboring structures have consistent locations and shapes that provide configurations and context that aid in segmentation. We define a maximum a posteriori (MAP) estimation framework using the constraining information provided by neighboring objects to segment several objects simultaneously. We introduce a representation for the joint density function of the neighbor objects, and define joint probability distributions over the variations of the neighboring shape and position relationships of a set of training images. In order to estimate the MAP shapes of the objects, we formulate the model in terms of level set functions, and compute the associated Euler-Lagrange equations. The contours evolve both according to the neighbor prior information and the image gray level information. This method is useful in situations where there is limited interobject information as opposed to robust global atlases. In addition, we compare our level set representation of the object shape to the point distribution model. Results and validation from experiments on synthetic data and medical imagery in two-dimensional and 3-D are demonstrated.

Accurate alignment of functional EPI data to anatomical MRI using a physics-based distortion model

Mapping of functional magnetic resonance imaging (fMRI) to conventional anatomical MRI is a valuable step in the interpretation of fMRI activations. One of the main limits on the accuracy of this alignment arises from differences in the geometric distortion induced by magnetic field inhomogeneity. This paper describes an approach to the registration of echo planar image (EPI) data to conventional anatomical images which takes into account this difference in geometric distortion. The authors make use of an additional spin echo EPI image and use the known signal conservation in spin echo distortion to derive a specialized multimodality nonrigid registration algorithm. They also examine a plausible modification using log-intensity evaluation of the criterion to provide increased sensitivity in areas of low EPI signal. A phantom-based imaging experiment is used to evaluate the behavior of the different criteria, comparing nonrigid displacement estimates to those provided by a magnetic field mapping acquisition. The algorithm is then applied to a range of nine brain imaging studies illustrating global and local improvement in the anatomical alignment and localization of fMRI activations.

Regional myocardial dysfunction during coronary angioplasty: Evaluation by two-dimensional echocardiography and 12 lead electrocardiography

Balloon inflation performed during percutaneous transluminal coronary angioplasty causes transient total occlusion of the coronary artery and thus provides a model for evaluation of the regional myocardial responses to transient ischemia. Twenty patients with normal left ventricular function undergoing angioplasty of isolated stenosis of the proximal left anterior descending coronary artery were studied. In group A (14 patients) analysis of one inflation-deflation sequence per patient was performed. Group B (six patients) had multiple (greater than 5) inflations; the first and last sequences were analyzed. Assessment included continuous two-dimensional echocardiography with computerized quantitative analysis of regional left ventricular wall motion, and continuous 12 lead electrocardiographic recordings. The mean duration of inflation in group A was 62 +/- 6 seconds (mean +/- SD). The onset of regional left ventricular dysfunction was 12 +/- 5 seconds after inflation. Profound dysfunction was noted in all patients. After 60 seconds of balloon occlusion of the coronary artery, 29% of patients had severe hypokinesia of the ischemic region and 71% had akinesia or dyskinesia. With deflation there was prompt recovery of regional function, with full recovery at 43 +/- 17 seconds. Comparison of data from first and last inflations in group B revealed no significant differences in time to onset of dysfunction, magnitude of dysfunction or time to complete recovery of function. The onset of ischemic electrocardiographic changes lagged behind the onset of wall motion abnormalities, with only 64% of patients showing evidence of ischemia on 12 lead electrocardiograms at 20 seconds of inflation. After 60 seconds, 86% had ischemia detectable by electrocardiography. Thus, balloon inflation during coronary angioplasty leads to profound but reversible regional left ventricular dysfunction. Repeated occlusions of the coronary artery during angioplasty do not have a cumulative ischemic effect. It may be hazardous to apply these findings to patients who have underlying major left ventricular dysfunction and in whom the reversibility of dysfunction and lack of cumulative ischemic effect may not be assured.