waashk/dblp huggingface dataset:
bayesian maximal paths for coronary artery segmentation from 3d ct angiograms. we propose a recursive bayesian model for the delineation of coronary arteries from 3d ct angiograms (cardiac cta) and discuss the use of discrete minimal path techniques as an efficient optimization scheme for the propagation of model realizations on a discrete graph. design issues such as the definition of a suitable accumulative metric are analyzed in the context of our probabilistic formulation.our approach jointly optimizes the vascular centerline and associated radius on a 4d space+scale graph. it employs a simple heuristic scheme to dynamically limit scale-space exploration for increased computational performance. it incorporates prior knowledge on radius variations and derives the local data likelihood from a multiscale, oriented gradient flux-based feature. from minimal cost path techniques, it inherits practical properties such as computational efficiency and workflow versatility. we quantitatively evaluated a two-point interactive implementation on a large and varied cardiac cta database. additionally, results from the rotterdam coronary artery algorithm evaluation framework are provided for comparison with existing techniques. the scores obtained are excellent (97.5% average overlap with ground truth delineated by experts) and demonstrate the high potential of the method in terms of robustness to anomalies and poor image quality.
a spatio-temporal atlas of the human fetal brain with application to tissue segmentation. modeling and analysis of mr images of the early developing human brain is a challenge because of the transient nature of different tissue classes during brain growth. to address this issue, a statistical model that can capture the spatial variation of structures over time is needed. here, we present an approach to building a spatio-temporal model of tissue distribution in the developing brain which can incorporate both developed tissues as well as transient tissue classes such as the germinal matrix by using constrained higher order polynomial models. this spatio-temporal model is created from a set of manual segmentations through groupwise registration and voxelwise non-linear modeling of tissue class membership, that allows us to represent the appearance as well as disappearance of the transient brain structures over time. applying this model to atlas-based segmentation, we generate age-specific tissue probability maps and use them to initialize an em segmentation of the fetal brain tissues. the approach is evaluated using clinical mr images of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. results indicate improvement in performance of atlas-based em segmentation provided by higher order temporal models that capture the variation of tissue occurrence over time.
a novel measure of fractional anisotropy based on the tensor distribution function. fractional anisotropy (fa), a very widely used measure of fiber integrity based on diffusion tensor imaging (dti), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. with high-angular resolution diffusion imaging (hardi) and the tensor distribution function (tdf), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. we found that fa, when compared with tdf-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. by contrast, mean diffusivity (md) as defined in standard dti appears to be more accurate. overall, we argue that novel measures derived from the tdf approach may yield more sensitive and accurate information than dti-derived measures.
a statistical model of right ventricle in tetralogy of fallot for prediction of remodelling and therapy planning. patients with repaired tetralogy of fallot commonly suffer from chronic pulmonary valve regurgitations and extremely dilated right ventricle (rv). to reduce risk factors, new pulmonary valves must be re-implanted. however, establishing the best timing for re-intervention is a clinical challenge because of the large variability in rv shape and in pathology evolution. this study aims at quantifying the regional impacts of growth and regurgitations upon the end-diastolic rv anatomy. the ultimate goal is to determine, among clinical variables, predictors for the shape in order to build a statistical model that predicts rv remodelling. the proposed approach relies on a forward model based on currents and lddmm algorithm to estimate an unbiased template of 18 patients and the deformations towards each individual shape. cross-sectional multivariate analyses are carried out to assess the effects of body surface area, tricuspid and transpulmonary valve regurgitations upon the rv shape. the statistically significant deformation modes were found clinically relevant. canonical correlation analysis yielded a generative model that was successfully tested on two new patients.
a log-euclidean polyaffine registration for articulated structures in medical images. in this paper we generalize the log-euclidean polyaffine registration framework of arsigny et al. [1] to deal with articulated structures. this framework has very useful properties as it guarantees the invertibility of smooth geometric transformations. in articulated registration a skeleton model is defined for rigid structures such as bones. the final transformation is affine for the bones and elastic for other tissues in the image. we extend the arsigny el al.'s method to deal with locally-affine registration of pairs of wires. this enables the possibility of using this registration framework to deal with articulated structures. in this context, the design of the weighting functions, which merge the affine transformations defined for each pair of wires, has a great impact not only on the final result of the registration algorithm, but also on the invertibility of the global elastic transformation. several experiments, using both synthetic images and hand radiographs, are also presented.
combining multiple true 3d ultrasound image volumes through re-registration and rasterization. we present an accurate and efficient technique to combine and rasterize multiple 3d ultrasound (3dus) image volumes originally presented in spherical coordinates into a single, 3d cartesian image that uniformly samples the total field of view. to ensure the consistency of merged image content in overlapping regions, image re-registration was performed by maximizing mutual information (mi). the technique was applied to 22 3dus image volumes obtained during five neurosurgical patient cases. the computational cost of the approach increases linearly with the number of images involved (average time to combine and rasterize one pair of 3dus images was 1.5 sec). interpolation was approximately 20% more accurate in overlapping regions when re-registration was performed before rasterization and minimized feature loss and/or blurring that was evident without re-registration. in addition, we report the average translational (35.2 mm) and rotational (38.5 o ) capture ranges for the mi re-registration of two volumetric 3dus images. the technique is applicable in any clinical application in which volumetric true 3dus is acquired.
automatic robust medical image registration using a new democratic vector optimization approach with multiple measures. the registration of various data is a challenging task in medical image processing and a highly frequented area of research. most of the published approaches tend to fail sporadically on different data sets. this happens due to two major problems. first, local optimization strategies induce a high risk when optimizing nonconvex functions. second, similarity measures might fail if they are not suitable for the data. thus, researchers began to combine multiple measures by weighted sums. in this paper, we show severe limitations of such summation approaches. we address both issues by a gradient-based vector optimization algorithm that uses multiple similarity measures. it gathers context information from the iteration process to detect and suppress failing measures. the new approach is evaluated by experiments from the field of 2d-3d registration. besides its generic character with respect to arbitrary data, the main benefit is a highly robust iteration behavior, where even very poor initial guesses of the transform result in good solutions.
spatiotemporal atlas estimation for developmental delay detection in longitudinal datasets. we propose a new methodology to analyze the anatomical variability of a set of longitudinal data (population scanned at several ages). this method accounts not only for the usual 3d anatomical variability (geometry of structures), but also for possible changes in the dynamics of evolution of the structures. it does not require that subjects are scanned the same number of times or at the same ages. first a regression model infers a continuous evolution of shapes from a set of observations of the same subject. second, spatiotemporal registrations deform jointly (1) the geometry of the evolving structure via 3d deformations and (2) the dynamics of evolution via time change functions. third, we infer from a population a prototype scenario of evolution and its 4d variability. our method is used to analyze the morphological evolution of 2d profiles of hominids skulls and to analyze brain growth from amygdala of autistics, developmental delay and control children.
accelerating feature based registration using the johnson-lindenstrauss lemma. we introduce an efficient search strategy to substantially accelerate feature based registration. previous feature based registration algorithms often use truncated search strategies in order to achieve small computation times. our new accelerated search strategy is based on the realization that the search for corresponding features can be dramatically accelerated by utilizing johnson-lindenstrauss dimension reduction. order of magnitude calculations for the search strategy we propose here indicate that the algorithm proposed is more than a million times faster than previously utilized naive search strategies, and this advantage in speed is directly translated into an advantage in accuracy as the fast speed enables more comparisons to be made in the same amount of time. we describe the accelerated scheme together with a full complexity analysis. the registration algorithm was applied to large transmission electron microscopy (tem) images of neural ultrastructure. our experiments demonstrate that our algorithm enables alignment of tem images with increased accuracy and efficiency compared to previous algorithms.
personalized pulmonary trunk modeling for intervention planning and valve assessment estimated from ct data. pulmonary valve disease affects a significant portion of the global population and often occurs in conjunction with other heart dysfunctions. emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. as minimal invasive procedures become common practice, imaging and non-invasive assessment techniques turn into key clinical tools. in this paper, we propose a novel approach for intervention planning as well as morphological and functional quantification of the pulmonary trunk and valve. an abstraction of the anatomic structures is represented through a four-dimensional, physiological model able to capture large pathological variation. a hierarchical estimation, based on robust learning methods, is applied to identify the patient-specific model parameters from volumetric ct scans. the algorithm involves detection of piecewise affine parameters, fast centre-line computation and local surface delineation. the estimated personalized model enables for efficient and precise quantification of function and morphology. this ability may have impact on the assessment and surgical interventions of the pulmonary valve and trunk. experiments performed on 50 cardiac computer tomography sequences demonstrated the average speed of 202 seconds and accuracy of 2.2mm for the proposed approach. an initial clinical validation yielded a significant correlation between model-based and expert measurements. to the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from ct data.
statistical detection of longitudinal changes between apparent diffusion coefficient images: application to multiple sclerosis. the automatic analysis of longitudinal changes between diffusion tensor imaging (dti) acquisitions is a promising tool for monitoring disease evolution. however, few works address this issue and existing methods are generally limited to the detection of changes between scalar images characterizing diffusion properties, such as fractional anisotropy or mean diffusivity, while richer information can be exploited from the whole set of apparent diffusion coefficient (adc) images that can be derived from a dti acquisition. in this paper, we present a general framework for detecting changes between two sets of adc images and we investigate the performance of four statistical tests. results are presented on both simulated and real data in the context of the follow-up of multiple sclerosis lesion evolution.
design and construction of a realistic dwi phantom for filtering performance assessment. a methodology to build a realistic phantom for the assessment of filtering performance in diffusion weighted images (dwi) is presented. from a real dwi data---set, a regularization process is carried out taking into account the diffusion model. this process drives to a model which accurately preserves the structural characteristics of actual dwi volumes, being in addition regular enough to be considered as a noise---free data---set and therefore to be used as a ground---truth. we compare our phantom with a kind of simplified phantoms commonly used in the literature (those based on homogeneous cross sections), concluding that the latter may introduce important biases in common quality measures used in the filtering performance assessment, and even drive to erroneous conclusions in the comparison of different filtering techniques.
towards guidance of electrophysiological procedures with real-time 3d intracardiac echocardiography fusion to c-arm ct. this paper describes a novel method for improving the navigation and guidance of devices and catheters in electrophysiology and interventional cardiology procedures using volumetric data fusion. the clinical workflow includes the acquisition and reconstruction of ct data from a c-arm x-ray angiographic system and the real-time acquisition of volumetric ultrasound datasets with a new intracardiac real-time 3d ultrasound catheter. mono- and multi-modal volumetric registration methods, as well as visualization modes, that are suitable for real-time fusion are described, which are the key components of this work. evaluation on phantom and in-vivo animal data shows that it is feasible to register and track the motion of real-time 3d intracardiac ultrasound in c-arm ct.
tensor-based analysis of genetic influences on brain integrity using dti in 100 twins. information from the full diffusion tensor (dt) was used to compute voxel-wise genetic contributions to brain fiber microstructure. first, we designed a new multivariate intraclass correlation formula in the log-euclidean framework [1]. we then analyzed used the full multivariate structure of the tensor in a multivariate version of a voxel-wise maximum-likelihood structural equation model (sem) that computes the variance contributions in the dts from genetic (a), common environmental (c) and unique environmental (e) factors. our algorithm was tested on dt images from 25 identical and 25 fraternal twin pairs. after linear and fluid registration to a mean template, we computed the intraclass correlation and falconer's heritability statistic for several scalar dt-derived measures and for the full multivariate tensors. covariance matrices were found from the dts, and inputted into sem. analyzing the full dt enhanced the detection of a and c effects. this approach should empower imaging genetics studies that use dti.
development of the ultra-miniaturized inertial measurement unit wb3 for objective skill analysis and assessment in neurosurgery: preliminary results. in recent years there has been an ever increasing amount of research and development of technologies and methodologies aimed at improving the safety of advanced surgery. in this context, several training methods and metrics have been proposed, in particular for laparoscopy, both to improve the surgeon's abilities and also to assess her/his skills. for neurosurgery, however, the extremely small movements and sizes involved have prevented until now the development of similar methodologies and systems.in this paper we present the development of the ultra-miniaturized inertial measurement unit wb3 (at present the smallest, lightest, and best performing in the world) for practical application in neurosurgery as skill assessment tool. this paper presents the feasibility study for quantitative discrimination of movements of experienced surgeons and beginners in a simple pick and place scenario.
on the manifold structure of the space of brain images. this paper investigates an approach to model the space of brain images through a low-dimensional manifold. a data driven method to learn a manifold from a collections of brain images is proposed. we hypothesize that the space spanned by a set of brain images can be captured, to some approximation, by a low-dimensional manifold, i.e. a parametrization of the set of images. the approach builds on recent advances in manifold learning that allow to uncover nonlinear trends in data. we combine this manifold learning with distance measures between images that capture shape, in order to learn the underlying structure of a database of brain images. the proposed method is generative. new images can be created from the manifold parametrization and existing images can be projected onto the manifold. by measuring projection distance of a held out set of brain images we evaluate the fit of the proposed manifold model to the data and we can compute statistical properties of the data using this manifold structure. we demonstrate this technology on a database of 436 mr brain images.
patient specific 4d coronary models from ecg-gated cta data for intra-operative dynamic alignment of cta with x-ray images. we present an approach to derive patient specific coronary models from ecg-gated cta data and their application for the alignment of cta with mono-plane x-ray imaging during interventional cardiology. a 4d (3d+t) deformation model of the coronary arteries is derived by (i) extraction of a 3d coronary model at an appropriate cardiac phase and (ii) non-rigid registration of the cta images at different ecg phases to obtain a deformation model. the resulting 4d coronary model is aligned with the x-ray data using a novel 2d+t/3d+t registration approach. model consistency and accuracy is evaluated using manually annotated coronary centerlines at systole and diastole as reference. improvement of registration robustness by using the 2d+t/3d+t registration is successfully demonstrated by comparison of the actual x-ray cardiac phase with the automatically determined best matching phase in the 4d coronary model.
alignment of viewing-angle dependent ultrasound images. we address the problem of the viewing-angle dependency of ultrasound images for registration. the reflected signal from large scale tissue boundaries is dependent on the incident angle of the beam. this applies an implicit weighting on the ultrasound image, dependent on the viewing-angle, which negatively affects the registration process, especially when utilizing curved linear transducers. we show that a simple reweighting of the images, considering a common physical model for ultrasound imaging, is not feasible. we therefore introduce a new matching function, separating reflectivity and scattering regions, which are the results of two different types of physical interactions of the ultrasound beam with the tissue. we use the local phase for identifying regions of reflectivity, and consider it as one part of our matching function, combining feature- and intensity-based aspects. first experiments provide good results for this novel registration approach.
constrained data decomposition and regression for analyzing healthy aging from fiber tract diffusion properties. it has been shown that brain structures in normal aging undergo significant changes attributed to neurodevelopmental and neurodegeneration processes as a lifelong, dynamic process. modeling changes in healthy aging will be necessary to explain differences to neurodegenerative patterns observed in mental illness and neurological disease. driving application is the analysis of brain white matter properties as a function of age, given a database of diffusion tensor images (dti) of 86 subjects well-balanced across adulthood. we present a methodology based on constrained pca (cpca) for fitting age-related changes of white matter diffusion of fiber tracts. it is shown that cpca applied to tract functions of diffusion isolates population noise and retains age as a smooth change over time, well represented by the first principal mode. cpca is therefore applied to a functional data analysis (fda) problem. age regression on tract functions reveals a nonlinear trajectory but also age-related changes varying locally along tracts. four tracts with four different tensor-derived scalar diffusion measures were analyzed, and leave-one-out validation of data compression is shown.
data-derived models for segmentation with application to surgical assessment and training. this paper addresses automatic skill assessment in robotic minimally invasive surgery. hidden markov models (hmms) are developed for individual surgical gestures (or surgemes) that comprise a typical bench-top surgical training task. it is known that such hmms can be used to recognize and segment surgemes in previously unseen trials [1]. here, the topology of each surgeme hmm is designed in a data-driven manner, mixing trials from multiple surgeons with varying skill levels, resulting in hmm states that model skill-specific sub-gestures. the sequence of hmm states visited while performing a surgeme are therefore indicative of the surgeon's skill level. this expectation is confirmed by the average edit distance between the state-level "transcripts" of the same surgeme performed by two surgeons with different expertise levels. some surgemes are further shown to be more indicative of skill than others.