waashk/dblp huggingface dataset:
segmentation of pathologic hearts in long-axis late-enhancement mri. we propose a new method to segment long-axis cardiac mr images acquired with a late-enhancement protocol. detecting the myocardium boundaries is difficult in these images because healthy myocardium appears dark while the intensity of enhanced areas ranges from gray to white, depending on the myocardial damage. in this context, geometrical template deformation, alternated with the update of a damaged tissue map, allows us to include abnormal myocardium parts in the final segmentation. the template and map are initialized using short-axis images and the deformation parameters are adapted according to the type of enhancement pattern. good segmentation results are obtained on a database of real pathologic heart images presenting various types of abnormal myocardium tissues.
contractile analysis with kriging based on mr myocardial velocity imaging. diagnosis and treatment of coronary artery disease requires a full understanding of the intrinsic contractile mechanics of the heart. mr myocardial velocity imaging is a promising technique for revealing intramural cardiac motion but its ability to depict 3d strain tensor distribution is constrained by anisotropic voxel coverage of velocity imaging due to limited imaging slices and the achievable snr in patient studies. this paper introduces a novel kriging estimator for simultaneously improving the tracking and dense inter-slice estimation of the myocardial velocity data. a harmonic embedding technique is employed to determine point correspondence between left ventricle models between subjects, allowing for a statistical shape model to be reconstructed. the use of different semivariograms is investigated for optimal deformation reconstruction. results from in vivo data demonstrate a marked improvement in tracking myocardial deformation, thus enhancing the potential clinical value of mr myocardial velocity imaging.
spatial weighed element based fem incorporating a priori information on bioluminescence tomography. bioluminescence tomography (blt) is a promising imaging technique which may dynamically and real-timely detect the molecular and cellular activity at the whole-body level in small animal studies. in view of the ill-posedness of the blt, it is hard to fully reconstruct source density. in addition, the uniqueness theorem on blt indicates that it is important to employ a priori information for accurate source reconstruction. hence, we adopt diffuse optical tomography (dot) technique to provide optical parameters of main tissues as a priori information. besides, we restrict the range of real source to a permissible region to raise the numerical stability and reduce the ill-posedness of blt. next, we forward spatial weighed element based finite element method and compare it's solutions with analytic formula and mose. numerical simulation of homogeneous and heterogeneous phantom demonstrates the source location and density with prior information is better than that not using a priori information.
averaging centerlines: mean shift on paths. generation of a reference standard from multiple manually annotated datasets is a non-trivial problem. this paper discusses the weighted averaging of 3d open curves, which we used to generate a reference standard for vessel tracking data. we show how weighted averaging can be implemented by applying the mean shift algorithm to paths, and discuss the details of our implementation. our approach can handle cases where the observer centerlines take different branches in a natural way. the method has been evaluated on synthetic data, and has been used to generate reference centerlines for evaluation of vessel tracking algorithms.
multi-attribute non-initializing texture reconstruction based active shape model (mantra). in this paper we present mantra (multi-attribute, non-initializing, texture reconstruction based active shape model) which incorporates a number of features that improve on the the popular active shape model (asm) algorithm. mantra has the following advantages over the traditional asm model. (1) it does not rely on image intensity information alone, as it incorporates multiple statistical texture features for boundary detection. (2) unlike traditional asms, mantra finds the border by maximizing a higher dimensional version of mutual information (mi) called combined mi (cmi), which is estimated from knn entropic graphs. the use of cmi helps to overcome limitations of the mahalanobis distance, and allows multiple texture features to be intelligently combined. (3) mantra does not rely on the mean pixel intensity values to find the border; instead, it reconstructs potential image patches, and the image patch with the best reconstruction based on cmi is considered the object border. our algorithm was quantitatively evaluated against expert ground truth on almost 230 clinical images (128 1.5 tesla (t) t2 weighted in vivo prostate magnetic resonance (mr) images, 78 dynamic contrast enhanced breast mr images, and 21 3t in vivo t1-weighted prostate mr images) via 6 different quantitative metrics. results from the more difficult prostate segmentation task (in which a second expert only had a 0.850 mean overlap with the first expert) show that the traditional asm method had a mean overlap of 0.668, while the mantra model had a mean overlap of 0.840.
streamline flows for white matter fibre pathway segmentation in diffusion mri. we introduce a fibre tract segmentation algorithm based on the geometric coherence of fibre orientations as indicated by a streamline flow model. the inference of local flow approximations motivates a pairwise consistency measure between fibre odf maxima. we use this measure in a recursive algorithm to cluster consistent odf maxima, leading to the segmentation of white matter pathways. the method requires minimal seeding compared to streamline tractography-based methods, and allows multiple tracts to pass through the same voxels. we illustrate the approach with a segmentation of the corpus callosum and one of the cortico-spinal tract, with each example seeded at a single voxel.
3d ultrasound-guided motion compensation system for beating heart mitral valve repair. beating heart intracardiac procedures promise significant benefits for patients, however, the fast motion of the heart poses serious challenges to surgeons. we present a new 3d ultrasound-guided motion (3dus) compensation system that synchronizes instrument motion with the heart. the system utilizes the fact that the motion of some intracardiac structures, including the mitral valve annulus, is largely constrained to translation along one axis. this allows the development of a real-time 3dus tissue tracker which we integrate with a 1 degree-of-freedom actuated surgical instrument, real-time 3dus instrument tracker, and predictive filter to devise a system with synchronization accuracy of 1.8 mm rmse. user studies involving the deployment of surgical anchors in a simulated mitral annuloplasty procedure demonstrate that the system increases success rates by over 100%. furthermore, it enables more careful anchor deployment by reducing forces to the tissue by 50% while allowing instruments to remain in contact with the tissue for longer periods.
new algorithms to map asymmetries of 3d surfaces. in this paper, we propose a set of new generic automated processing tools to characterise the local asymmetries of anatomical structures (represented by surfaces) at an individual level, and within/between populations. the building bricks of this toolbox are: 1) a new algorithm for robust, accurate, and fast estimation of the symmetry plane of grossly symmetrical surfaces, and 2) a new algorithm for the fast, dense, non-linear matching of surfaces. this last algorithm is used both to compute dense individual asymmetry maps on surfaces, and to register these maps to a common template for population studies. we show these two algorithms to be mathematically well-grounded, and provide some validation experiments. then we propose a pipeline for the statistical evaluation of local asymmetries within and between populations. finally we present some results on real data.
r-plus: a riemannian anisotropic edge detection scheme for vascular segmentation. in this paper, detection of edges in oriented fields is addressed. in some applications such as vessel segmentation because of the intrinsic orientation of the structures, edge detection is only demanded in a particular subspace. this is specially usefull when a curve evolution is chosen for segmentation since gradients in parallel to vessel orientation may stop the contour. an anisotropic edge detection scheme is generalized on a riemannian manifold using the local structure tensor. the method is the generalization of the plus operator proposed in [8] for accurate curved edge detection. examples are given and the comparison is made with the state-of-the-art flux maximizing flow which indicates that significant improvements in terms of leakage minimization and thiner vessel delineation is achievable using our methodology.
improving parenchyma segmentation by simultaneous estimation of tissue property t1 map and group-wise registration of inversion recovery mr breast images. the parenchyma tissue in the breast has a strong relation with predictive biomarkers of breast cancer. to better segment parenchyma, we perform segmentation on estimated tissue property t 1 map. to improve the estimation of tissue property (t 1) which is the basis for parenchyma segmentation, we present an integrated algorithm for simultaneous t 1 map estimation, t 1 map based parenchyma segmentation and group-wise registration on series of inversion recovery magnetic resonance (mr) breast images. the advantage of using this integrated algorithm is that the simultaneous t 1 map estimation (e-step) and group-wise registration (r-step) could benefit each other and jointly improve parenchyma segmentation. in particular, in e-step, t 1 map based segmentation could help perform an edge-preserving smoothing on the tentatively estimated noisy t 1 map, and could also help provide tissue probability maps to be robustly registered in r-step. meanwhile, the improved estimation of t 1 map could help segment parenchyma in a more accurate way. in r-step, for robust registration, the group-wise registration is performed on the tissue probability maps produced in e-step, rather than the original inversion recovery mr images, since tissue probability maps are the intrinsic tissue property which is invariant to the use of different imaging parameters. the better alignment of images achieved in r-step can help improve t 1 map estimation and indirectly the t 1 map based parenchyma segmentation. by iteratively performing e-step and r-step, we can simultaneously obtain better results for t 1 map estimation,t 1 map based segmentation, group-wise registration, and finally parenchyma segmentation.
model-based segmentation using graph representations. a generic supervised segmentation approach is presented. the object is described as a graph where the vertices correspond to landmarks points and the edges define the landmark relations. instead of building one single global shape model, a priori shape information is represented as a concatenation of local shape models that consider only local dependencies between connected landmarks. the objective function is obtained from a maximum a posteriori criterion and is build up of localized energies of both shape and landmark intensity information. the optimization problem is discretized by searching candidates for each landmark using individual landmark intensity descriptors. the discrete optimization problem is then solved using mean field annealing or dynamic programming techniques. the algorithm is validated for hand bone segmentation from rx datasets and for 3d liver segmentation from contrast enhanced ct images.
spatial consistency in 3d tract-based clustering statistics. we propose a novel technique for tract-based comparison of dti-indices between groups, based on a representation that is estimated while matching fiber tracts. the method involves a non-rigid registration based on a joint clustering and matching approach, after which a 3d-atlas of cluster center points is used as a frame of reference for statistics. patient and control fa-distributions are compared per cluster. spatial consistency is taken to reflect a significant difference between groups. accordingly, a non-parametric classification is performed to assess the continuity of pathology over larger tract regions. in a study to infant survivors treated for medulloblastoma with intravenous methotrexate and cranial radiotherapy, significant decreases in fa in major parts of the corpus callosum were found.
surface-based structural group analysis of fmri data. as structural and surface-based analyses gain interest for activation detection, morphometry and intersubject matching purposes, this paper proposes a method to perform structural group analyses directly on the cortical surface. scale-space blobs are extracted from surface-based functional maps and matched across subjects. the process aims at identifying activations within a population despite the various effects due to variability. results of the method are presented with simulated activations and with data from a somatotopy protocol.
a novel algorithm for heart motion analysis based on geometric constraints. recently, much attention has been focused on heart motion analysis for minimally invasive beating-heart surgery. unfortunately existing techniques usually require the camera(s) to be fixed during the motion analysis, which can restrict its usefulness during surgery. in this paper we present a novel method for heart motion analysis using geometric constraint, which can estimate the motion from a moving camera and employ multiple image features to improve robustness to noise. our approach combines the benefits of geometry estimation for obtaining an accurate and robust solution with the proper treatment of respiratory motion. the proposed method can be applied to not only beating heart surgery, but also to other procedures involving periodic organ motion, such as lung and liver.
particle-based shape analysis of multi-object complexes. this paper presents a new method for optimizing surface point correspondences for shape modeling of multiobject anatomy, or shape complexes. the proposed method is novel in that it optimizes correspondence positions in the full, joint shape space of the object complex. researchers have previously only considered the correspondence problem separately for each structure, thus ignoring the interstructural shape correlations that are increasingly of interest in many clinical contexts, such as the study of the effects of disease on groups of neuroanatomical structures. the proposed method uses a nonparametric, dynamic particle system to simultaneously sample object surfaces and optimize correspondence point positions. this paper also suggests a principled approach to hypothesis testing using the hotelling t 2 test in the pca space of the correspondence model, with a simulation-based choice of the number of pca modes. we also consider statistical analysis of object poses. the modeling and analysis methods are illustrated on brain structure complexes from an ongoing clinical study of pediatric autism.
comprehensive segmentation of cine cardiac mr images. a typical cardiac magnetic resonance (cmr) examination includes acquisition of a sequence of short-axis (sa) and long-axis (la) images covering the cardiac cycle. quantitative analysis of the heart function requires segmentation of the left ventricle (lv) sa images, while segmented la views allow more accurate estimation of the basal slice and can be used for slice registration. since manual segmentation of cmr images is very tedious and time-consuming, its automation is highly required. in this paper, we propose a fully automatic 2d method for segmenting lv consecutively in la and sa images. the approach was validated on 35 patients giving mean segmentation error smaller than one pixel, both for la and sa, and accurate lv volume measurements.
customized design of hearing aids using statistical shape learning. 3d shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient's anatomy. in this paper, we present a solution to the problem of customized 3d shape modeling using a statistical shape analysis framework. we design a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation. the two associated shape classes are represented in a lower dimensional manifold, and the reduced set of parameters obtained in this subspace is utilized in an estimation, which is exemplified by a multivariate regression in this paper. we demonstrate our method with a felicitous application to estimation of customized hearing aid devices.
automatic image analysis of histopathology specimens using concave vertex graph. automatic image analysis of histopathology specimens would help the early detection of blood cancer. the first step for automatic image analysis is segmentation. however, touching cells bring the difficulty for traditional segmentation algorithms. in this paper, we propose a novel algorithm which can reliably handle touching cells segmentation. robust estimation and color active contour models are used to delineate the outer boundary. concave points on the boundary and inner edges are automatically detected. a concave vertex graph is constructed from these points and edges. by minimizing a cost function based on morphological characteristics, we recursively calculate the optimal path in the graph to separate the touching cells. the algorithm is computationally efficient and has been tested on two large clinical dataset which contain 207 images and 3898 images respectively. our algorithm provides better results than other studies reported in the recent literature.
towards multi-directional oct for speckle noise reduction. multi-directional optical coherence tomography (md-oct) applies and extends the concept of angular compounding for speckle noise reduction to the area of oct imaging. oct images are acquired from a wide range of angles of view. averaging of the rotated images therefore requires compensation of the parallax which is achieved by simple image registration for image reconstruction. test measurements of a sample structure in a low and highly scattering environment show that the method improves the signal-to-noise ratio by a factor of 4 and hence reduces speckle noise significantly. experimental results also show that the proposed averaging increases the performance of common edge-detection algorithms.
on computing the underlying fiber directions from the diffusion orientation distribution function. in this work, a novel method for determining the principal directions (maxima) of the diffusion orientation distribution function(odf) is proposed. we represent the odf as a symmetric high-order cartesian tensor restricted to the unit sphere and show that the extrema of the odf are solutions to a system of polynomial equations whose coefficients are polynomial functions of the tensor elements. in addition to demonstrating the ability of our methods to identify the principal directions in real data, we show that this method correctly identifies the principal directions under a range of noise levels. we also propose the use of the principal curvatures of the graph of the odf function as a measure of the degree of diffusion anisotropy in that direction. we present simulated results illustrating the relationship between the mean principal curvature, measured at the maxima, and the fractional anisotropy of the underlying diffusion tensor.
kinetic modeling based probabilistic segmentation for molecular images. we propose a semi-supervised, kinetic modeling based segmentation technique for molecular imaging applications. it is an iterative, self-learning algorithm based on uncertainty principles, designed to alleviate low signal-to-noise ratio (snr) and partial volume effect (pve) problems. synthetic fluorodeoxyglucose (fdg) and simulated raclopride dynamic positron emission tomography (dpet) brain images with excessive noise levels are used to validate our algorithm. we show, qualitatively and quantitatively, that our algorithm outperforms state-of-the-art techniques in identifying different functional regions and recovering the kinetic parameters.
task-specific functional brain geometry from model maps. in this paper we propose model maps to derive and represent the intrinsic functional geometry of a brain from functional magnetic resonance imaging (fmri) data for a specific task. model maps represent the coherence of behavior of individual fmri-measurements for a set of observations, or a time sequence. the maps establish a relation between individual positions in the brain by encoding the blood oxygen level dependent (bold) signal over a time period in a markov chain. they represent this relation by mapping spatial positions to a new metric space, the model map. in this map the euclidean distance between two points relates to the joint modeling behavior of their signals and thus the co-dependencies of the corresponding signals. the map reflects the functional as opposed to the anatomical geometry of the brain. it provides a quantitative tool to explore and study global and local patterns of resource allocation in the brain. to demonstrate the merit of this representation, we report quantitative experimental results on 29 fmri time sequences, each with sub-sequences corresponding to 4 different conditions for two groups of individuals. we demonstrate that drug abusers exhibit lower differentiation in brain interactivity between baseline and reward related tasks, which could not be quantified until now.
segmenting brain tumors using pseudo-conditional random fields. locating brain tumor segmentation within mr (magnetic resonance) images is integral to the treatment of brain cancer. this segmentation task requires classifying each voxel as either tumor or non-tumor, based on a description of that voxel. unfortunately, standard classifiers, such as logistic regression (lr) and support vector machines (svm), typically have limited accuracy as they treat voxels as independent and identically distributed (iid). approaches based on random fields, which are able to incorporate spatial constraints, have recently been applied to brain tumor segmentation with notable performance improvement over iid classifiers. however, previous random field systems involved computationally intractable formulations, which are typically solved using some approximation. here, we present pseudo-conditional random fields (pcrfs), which achieve accuracy similar to other random fields variants, but are significantly more efficient. we formulate a pcrf as a regularized discriminative classifier that relaxes the classification decision for each voxel by considering the labels and features of neighboring voxels.
volume reconstruction by inverse interpolation: application to interleaved mr motion correction. we introduce in this work a novel algorithm for volume reconstruction from data acquired on an irregular grid, e.g., from multiple co-registered images. the algorithm, which is based on an inverse interpolation formalism, is superior to other methods in particular when the input images have lower spatial resolution than the reconstructed image. local intensity bounds are enforced by an l-bfgs-b optimizer, regularize the reconstruction problem, and preserve the intensity distribution of the input images. we demonstrate the usefulness of our method by applying it to retrospective motion correction in interleaved mr images.