waashk/dblp huggingface dataset:
an inverse scattering algorithm for the segmentation of the luminal border on intravascular ultrasound data. intravascular ultrasound (ivus) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels and is particularly useful for studying atherosclerosis. in this paper, we present a novel method for segmentation of the luminal border on ivus images using the radio frequency (rf) raw signal based on a scattering model and an inversion scheme. the scattering model is based on a random distribution of point scatterers in the vessel. the per-scatterer signal uses a differential backscatter cross-section coefficient (dbc) that depends on the tissue type. segmentation requires two inversions: a calibration inversion and a reconstruction inversion. in the calibration step, we use a single manually segmented frame and then solve an inverse problem to recover the dbc for the lumen and vessel wall (¿ l and ¿ w , respectively) and the width of the impulse signal ¿. in the reconstruction step, we use the parameters from the calibration step to solve a new inverse problem: for each angle ¿ i of the ivus data, we reconstruct the lumen-vessel wall interface. we evaluated our method using three 40mhz ivus sequences by comparing with manual segmentations. our preliminary results indicate that it is possible to segment the luminal border by solving an inverse problem using the ivus rf raw signal with the scatterer model.
a novel 3d joint markov-gibbs model for extracting blood vessels from pc-mra images. new techniques for more accurate segmentation of a 3d cerebrovascular system from phase contrast (pc) magnetic resonance angiography (mra) data are proposed. in this paper, we describe pc---mra images and desired maps of regions by a joint markov-gibbs random field model (mgrf) of independent image signals and interdependent region labels but focus on most accurate model identification. to better specify region borders, each empirical distribution of signals is precisely approximated by a linear combination of discrete gaussians (lcdg) with positive and negative components. we modified the conventional expectation-maximization (em) algorithm to deal with the lcdg. the initial segmentation based on the lcdg-models is then iteratively refined using a mgrf model with analytically estimated potentials. experiments with both the phantoms and real data sets confirm high accuracy of the proposed approach.
high-quality model generation for finite element simulation of tissue deformation. in finite element simulation, size, shape, and placement of the elements in a model are significant factors that affect the interpolation and numerical errors of a solution. in medical simulations, such models are desired to have higher accuracy near features such as anatomical boundaries (surfaces) and they are often required to have element faces lying along these surfaces. conventional modelling schemes consist of a segmentation step delineating the anatomy followed by a meshing step generating elements conforming to this segmentation. in this paper, a one-step energy-based model generation technique is proposed. an objective function is minimized when each element of a mesh covers similar image intensities while, at the same time, having desirable fem characteristics. such a mesh becomes essential for accurate models for deformation simulation, especially when the image intensities represent a mechanical feature of the tissue such as the elastic modulus. the use of the proposed mesh optimization is demonstrated on synthetic phantoms, 2d/3d brain mr images, and prostate ultrasound-elastography data.
shape analysis of human brain interhemispheric fissure bending in mri. this paper introduces a novel approach to analyze yakovlevian torque by quantifying the bending of human brain interhemispheric fissure in three-dimensional magnetic resonance imaging. it extracts the longitudinal medial surface between the cerebral hemispheres, which are segmented with an accurate and completely automatic technique, as the shape representation of the interhemispheric fissure. the extracted medial surface is modeled with a polynomial surface through least-square fitting. finally, curvature features, e.g. principal, gaussian and mean curvatures, are computed at each point of the fitted medial surface to describe the local bending of the interhemispheric fissure. this method was applied to clinical images of healthy controls (12 males, 7 females) and never-medicated schizophrenic subjects (11 males, 7 females). the hypothesis of the normal interhemispheric fissure bending (rightward in the occipital region) was quantitatively demonstrated. moreover, we found significant differences (p < 0.05) between the male schizophrenics and healthy controls with respect to the interhemispheric fissure bending in the frontal and occipital regions. these results show that our method is applicable for studying abnormal yakovlevian torque related to mental diseases.
particle based shape regression of open surfaces with applications to developmental neuroimaging. shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. in this paper we propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. the statistical significance of the dependence is evaluated using permutation tests designed to estimate the likelihood of achieving the observed statistics under numerous rearrangements of the shape parameters with respect to the explanatory variable. we demonstrate the method on synthetic data and provide a new results on clinical mri data related to early development of the human head.
conditional variability of statistical shape models based on surrogate variables. we propose to increment a statistical shape model with surrogate variables such as anatomical measurements and patient-related information, allowing conditioning the shape distribution to follow prescribed anatomical constraints. the method is applied to a shape model of the human femur, modeling the joint density of shape and anatomical parameters as a kernel density. results show that it allows for a fast, intuitive and anatomically meaningful control on the shape deformations and an effective conditioning of the shape distribution, allowing the analysis of the remaining shape variability and relations between shape and anatomy. the approach can be further employed for initializing elastic registration methods such as active shape models, improving their regularization term and reducing the search space for the optimization.
an object-based method for rician noise estimation in mr images. the estimation of the noise level in mr images is used to assess the consistency of statistical analysis or as an input parameter in some image processing techniques. most of the existing rician noise estimation methods are based on background statistics, and as such are sensitive to ghosting artifacts. in this paper, a new object-based method is proposed. this method is based on the adaptation of the median absolute deviation (mad) estimator in the wavelet domain for rician noise. the adaptation for rician noise is performed by using only the wavelet coefficients corresponding to the object and by correcting the estimation with an iterative scheme based on the snr of the image. a quantitative validation on synthetic phantom with artefacts is presented and a new validation framework is proposed to perform quantitative validation on real data. the results show the accuracy and the robustness of the proposed method.
cortical shape analysis in the laplace-beltrami feature space. for the automated analysis of cortical morphometry, it is critical to develop robust descriptions of the position of anatomical structures on the convoluted cortex. using the eigenfunction of the laplace-beltrami operator, we propose in this paper a novel feature space to characterize the cortical geometry. derived from intrinsic geometry, this feature space is invariant to scale and pose variations, anatomically meaningful, and robust across population. a learning-based sulci detection algorithm is developed in this feature space to demonstrate its application in cortical shape analysis. automated sulci detection results with 10 training and 15 testing surfaces are presented.
depth data improves skin lesion segmentation. this paper shows that adding 3d depth information to rgb colour images improves segmentation of pigmented and non-pigmented skin lesion. a region-based active contour segmentation approach using a statistical model based on the level-set framework is presented. we consider what kinds of properties (e.g., colour, depth, texture) are most discriminative. the experiments show that our proposed method integrating chromatic and geometric information produces segmentation results for pigmented lesions close to dermatologists and more consistent and accurate results for non-pigmented lesions.
towards accurate, automatic segmentation of the hippocampus and amygdala from mri. we describe progress towards fully automatic segmentation of the hippocampus (hc) and amygdala (ag) in human subjects from mri data. three methods are described and tested with a set of mris from 80 young normal controls, using manual labeling of the hc and ag as a gold standard. the methods include: 1) our animal atlas-based method that uses non-linear registration to a pre-labeled non-linear average template (icbm152). hc and ag labels, defined on the template are mapped through the inverse transformation to segment these structures on the subject's mri; 2) template-based segmentation, where we select the most similar mri from the set of 80 labeled datasets to use as a template in the standard animal segmentation scheme; 3) label fusion methods where we combine segmentations from the `n' most similar templates. the label fusion technique yields the best results with median kappas of 0.886 and 0.826 for hc and ag, respectively.
gender differences in cerebral cortical folding: multivariate complexity-shape analysis with insights into handling brain-volume differences. this paper presents a study of gender differences in adult human cerebral cortical folding patterns. the study employs a new multivariate statistical descriptor for analyzing folding patterns in a region of interest (roi) and a rigorous nonparametric permutation-based scheme for hypothesis testing. unlike typical roi-based methods that summarize folding complexity or shape by single/few numbers, the proposed descriptor systematically constructs a unified description of complexity and shape in a high-dimensional space (thousands of numbers/dimensions). furthermore, this paper presents new mathematical insights into the relationship of intra-cranial volume (icv) with cortical complexity and shows that conventional complexity descriptors implicitly handle icv differences in different ways, thereby lending different meanings to "complexity". this paper describes two systematic methods for handling icv changes in folding studies using the proposed descriptor. the clinical study in this paper exploits these theoretical insights to demonstrate that (i) the answer to which gender has higher/lower "complexity" depends on how a folding measure handles icv differences and (ii) cortical folds in males and females differ significantly in shape as well.
intrinsic regression models for manifold-valued data. in medical imaging analysis and computer vision, there is a growing interest in analyzing various manifold-valued data including 3d rotations, planar shapes, oriented or directed directions, the grassmann manifold, deformation field, symmetric positive definite (spd) matrices and medial shape representations (m-rep) of subcortical structures. particularly, the scientific interests of most population studies focus on establishing the associations between a set of covariates (e.g., diagnostic status, age, and gender) and manifold-valued data for characterizing brain structure and shape differences, thus requiring a regression modeling framework for manifold-valued data. the aim of this paper is to develop an intrinsic regression model for the analysis of manifold-valued data as responses in a riemannian manifold and their association with a set of covariates, such as age and gender, in euclidean space. because manifold-valued data do not form a vector space, directly applying classical multivariate regression may be inadequate in establishing the relationship between manifold-valued data and covariates of interest, such as age and gender, in real applications. our intrinsic regression model, which is a semiparametric model, uses a link function to map from the euclidean space of covariates to the riemannian manifold of manifold data. we develop an estimation procedure to calculate an intrinsic least square estimator and establish its limiting distribution. we develop score statistics to test linear hypotheses on unknown parameters. we apply our methods to the detection of the difference in the morphological changes of the left and right hippocampi between schizophrenia patients and healthy controls using medial shape description.
mesh generation from 3d multi-material images. the problem of generating realistic computer models of objects represented by 3d segmented images is important in many biomedical applications. labelled 3d images impose particular challenges for meshing algorithms because multi-material junctions form features such as surface pacthes, edges and corners which need to be preserved into the output mesh. in this paper, we propose a feature preserving delaunay refinement algorithm which can be used to generate high-quality tetrahedral meshes from segmented images. the idea is to explicitly sample corners and edges from the input image and to constrain the delaunay refinement algorithm to preserve these features in addition to the surface patches. our experimental results on segmented medical images have shown that, within a few seconds, the algorithm outputs a tetrahedral mesh in which each material is represented as a consistent submesh without gaps and overlaps. the optimization property of the delaunay triangulation makes these meshes suitable for the purpose of realistic visualization or finite element simulations.
left ventricle segmentation via graph cut distribution matching. we present a discrete kernel density matching energy for segmenting the left ventricle cavity in cardiac magnetic resonance sequences. the energy and its graph cut optimization based on an original first-order approximation of the bhattacharyya measure have not been proposed previously, and yield competitive results in nearly real-time. the algorithm seeks a region within each frame by optimization of two priors, one geometric (distance-based) and the other photometric, each measuring a distribution similarity between the region and a model learned from the first frame. based on global rather than pixelwise information, the proposed algorithm does not require complex training and optimization with respect to geometric transformations. unlike related active contour methods, it does not compute iterative updates of computationally expensive kernel densities. furthermore, the proposed first-order analysis can be used for other intractable energies and, therefore, can lead to segmentation algorithms which share the flexibility of active contours and computational advantages of graph cuts. quantitative evaluations over 2280 images acquired from 20 subjects demonstrated that the results correlate well with independent manual segmentations by an expert.
surface/volume-based articulated 3d spine inference through markov random fields. this paper presents a method towards inferring personalized 3d spine models to intraoperative ct data acquired for corrective spinal surgery. an accurate 3d reconstruction from standard x-rays is obtained before surgery to provide the geometry of vertebrae. the outcome of this procedure is used as basis to derive an articulated spine model that is represented by consecutive sets of intervertebral articulations relative to rotation and translation parameters (6 degrees of freedom). inference with respect to the model parameters is then performed using an integrated and interconnected markov random field graph that involves singleton and pairwise costs. singleton potentials measure the support from the data (surface or image-based) with respect to the model parameters, while pairwise constraints encode geometrical dependencies between vertebrae. optimization of model parameters in a multi-modal context is achieved using efficient linear programming and duality. we show successful image registration results from simulated and real data experiments aimed for image-guidance fusion.
setting priors and enforcing constraints on matches for nonlinear registration of meshes. we show that a simple probabilistic modelling of the registration problem for surfaces allows to solve it by using standard clustering techniques. in this framework, point-to-point correspondences are hypothesized between the two free-form surfaces, and we show how to specify priors and to enforce global constraints on these matches with only minor changes to the optimisation algorithm. the purpose of these two modifications is to increase its capture range and to obtain more realistic geometrical transformations between the surfaces. we conclude with some validation experiments and results on synthetic and real data.
use of simulated atrophy for performance analysis of brain atrophy estimation approaches. in this paper, we study the performance of popular brain atrophy estimation algorithms using a simulated gold standard. the availability of a gold standard facilitates a sound evaluation of the measures of atrophy estimation, which is otherwise complicated. firstly, we propose an approach for the construction of a gold standard. it involves the simulation of a realistic brain tissue loss based on the estimation of a topology preserving b-spline based deformation fields. using this gold standard, we present an evaluation of three standard brain atrophy estimation methods (siena, sienax and bsi) in the presence of bias field inhomogeneity and noise. the effect of brain lesion load on the measured atrophy is also evaluated. our experiments demonstrate that siena, sienax and bsi show a deterioration in their performance in the presence of bias field inhomogeneity and noise. the observed mean absolute errors in the measured percentage of brain volume change (pbvc) are 0.35%±0.38, 2.03%±1.46 and 0.91%±0.80 for siena, sienax and bsi, respectively, for simulated whole brain atrophies in the range 0 ¿ 1%.
incompressible cardiac motion estimation of the left ventricle using tagged mr images. interpolation from sparse imaging data is typically required to achieve dense, three-dimensional quantification of left ventricular function. although the heart muscle is known to be incompressible, this fact is ignored by most previous approaches that address this problem. in this paper, we present a method to reconstruct a dense representation of the three-dimensional, incompressible deformation of the left ventricle from tagged mr images acquired in both short-axis and long axis orientations. the approach applies a smoothing, divergence-free, vector spline to interpolate velocity fields at intermediate discrete times such that the collection of velocity fields integrate over time to match the observed displacement components. through this process, the method yields a dense estimate of a displacement field that matches our observations and also corresponds to an incompressible motion.
3d multi-branch tubular surface and centerline extraction with 4d iterative key points. an innovative 3d multi-branch tubular structure and centerline extraction method is proposed in this paper. in contrast to classical minimal path techniques that can only detect a single curve between two pre-defined initial points, this method propagates outward from only one initial seed point to detect 3d multi-branch tubular surfaces and centerlines simultaneously. first, instead of only representing the trajectory of a tubular structure as a 3d curve, the surface of the entire structure is represented as a 4d curve along which every point represents a 3d sphere inside the tubular structure. then, from any given sphere inside the tubular structure, a novel 4d iterative key point searching scheme is applied, in which the minimal action map and the euclidean length map are calculated with a 4d freezing fast marching evolution. a set of 4d key points is obtained during the front propagation process. finally, by sliding back from each key point to the previous one via the minimal action map until all the key points are visited, we are able to fully obtain global minimizing multi-branch tubular surfaces. an additional immediate benefit of this method is a natural notion of a multi-branch tube's "central curve" by taking only the first three spatial coordinates of the detected 4d multi-branch curve. experimental results on 2d/3d medical vascular images illustrate the benefits of this method.
actin filament tracking based on particle filters and stretching open active contour models. we introduce a novel algorithm for actin filament tracking and elongation measurement. particle filters (pf) and stretching open active contours (soac) work cooperatively to simplify the modeling of pf in a one-dimensional state space while naturally integrating filament body constraints to tip estimation. our algorithm reduces the pf state spaces to one-dimensional spaces by tracking filament bodies using soac and probabilistically estimating tip locations along the curve length of soacs. experimental evaluation on tirfm image sequences with very low snrs demonstrates the accuracy and robustness of this approach.
atlas-based improved prediction of magnetic field inhomogeneity for distortion correction of epi data. we describe a method for atlas-based segmentation of structural mri for calculation of magnetic fieldmaps. ct data sets are used to construct a probabilistic atlas of the head and corresponding mr is used to train a classifier that segments soft tissue, air, and bone. subject-specific fieldmaps are computed from the segmentations using a perturbation field model. previous work has shown that distortion in echo-planar images can be corrected using predicted fieldmaps. we obtain results that agree well with acquired fieldmaps: 90% of voxel shifts from predicted fieldmaps show subvoxel disagreement with those computed from acquired fieldmaps. in addition, our fieldmap predictions show statistically significant improvement following inclusion of the atlas.
model-based estimation of ventricular deformation in the cat brain. the estimation of ventricular deformation has important clinical implications related to neuro-structural disorders such as hydrocephalus. in this paper, a poroelastic model was used to represent deformation effects resulting from the ventricular system and was studied in 5 feline experiments. chronic or acute hydrocephalus was induced by injection of kaolin into the cisterna magna or saline into the ventricles; a catheter was then inserted in the lateral ventricle to drain the fluid out of the brain. the measured displacement data which was extracted from pre-drainage and post-drainage mr images were incorporated into the model through the adjoint equations method. the results indicate that the computational model of the brain and ventricular system captured 33% of the ventricle deformation on average and the model-predicted intraventricular pressure was accurate to 90% of the recorded value during the chronic hydrocephalus experiments.