waashk/dblp huggingface dataset:
dynamic cardiac mapping on patient-specific cardiac models. minimally invasive techniques for electrophysiological cardiac data mapping and catheter ablation therapy have been driven through advancements in computer-aided technologies, including magnetic tracking systems, and virtual and augmented-reality environments. the objective of this work is to extend current cardiac mapping techniques to collect and display data in the temporal domain, while mapping on patient-specific cardiac models. this paper details novel approaches to collecting spatially tracked cardiac electrograms, registering the data with a patient-specific cardiac model, and interpreting the data directly on the model surface, with the goal of giving a more comprehensive cardiac mapping system in comparison to current systems. to validate the system, laboratory studies were conducted to assess the accuracy of navigating to both physical and virtual landmarks. subsequent to the laboratory studies, an in-vivo porcine experiment was conducted to assess the systems overall ability to collect spatial tracked electrophysiological data, and map directly onto a cardiac model. the results from these experiments show the new dynamic cardiac mapping system was able to maintain high accuracy of locating physical and virtual landmarks, while creating a dynamic cardiac map displayed on a dynamic cardiac surface model.
dynamic model-driven quantitative and visual evaluation of the aortic valve from 4d ct. aortic valve disease is an important cardio-vascular disorder, which affects 2.5% of the global population and often requires elaborate clinical management. experts agree that visual and quantitative evaluation of the valve, crucial throughout the clinical workflow, is currently limited to 2d imaging which can potentially yield inaccurate measurements. in this paper, we propose a novel approach for morphological and functional quantification of the aortic valve based on a 4d model estimated from computed tomography data. a physiological model of the aortic valve, capable to express large shape variations, is generated using parametric splines together with anatomically-driven topological and geometrical constraints. recent advances in discriminative learning and incremental searching methods allow rapid estimation of the model parameters from 4d cardiac ct specifically for each patient. the proposed approach enables precise valve evaluation with model-based dynamic measurements and advanced visualization. extensive experiments and initial clinical validation demonstrate the efficiency and accuracy of the proposed approach. to the best of our knowledge this is the first time such a patient specific 4d aortic valve model is proposed.
automatic detection of calcified coronary plaques in computed tomography data sets. the detection of calcified plaques is an essential step in the assessment of coronary heart diseases. however, manual plaque segmentation is subjected to intra- and inter-observer variability. we present a novel framework for the automatic detection of calcified coronary plaques in computed tomography images. in contrast to the state-of-the-art, both the native and the angio data sets are included to gain additional information about each plaque for its detection and subsequent assessment. the framework was successfully tested on 127 patients where 85.5% of the calcified and 96% of the obstructive plaques have been detected.
bone segmentation and fracture detection in ultrasound using 3d local phase features. 3d ultrasound (us) is increasingly considered as a viable alternative imaging modality in computer-assisted orthopaedic surgery (caos) applications. automatic bone segmentation from us images, however, remains a challenge due to speckle noise and various other artifacts inherent to us. in this paper, we present intensity invariant three dimensional (3d) local image phase features, obtained using 3d log-gabor filter banks, for extracting ridge-like features similar to those that occur at soft tissue/bone interfaces. our contributions include the novel extension of 2d phase symmetry features to 3d and their use in automatic extraction of bone surfaces and fractured fragments in 3d us. we validate our technique using phantom, in vitro, and in vivo experiments. qualitative and quantitative results demonstrate remarkably clear segmentations results of bone surfaces with a localization accuracy of better than 0.62mm and mean errors in estimating fracture displacements below 0.65mm, which will likely be of strong clinical utility.
on-the-fly motion-compensated cone-beam ct using an a priori motion model. respiratory motion causes artifacts in slow-rotating cone-beam (cb) computed tomography (ct) images acquired for example for image guidance of radiotherapy. respiration-correlated cbct has been proposed to correct for the respiratory motion, but the use of a subset of the cb projections to reconstruct each frame of the 4d cbct image limits their quality, thus requiring a longer acquisition time. another solution is motion-compensated cbct which consists of reconstructing a single 3d cbct image at a reference position from all the cb projections by using an estimate of the respiratory motion in the reconstruction algorithm. in this paper, we propose a method for motion-compensated cbct which allows to reconstruct the image on-the-fly, i.e. concurrent with acquisition. before the cb acquisition, a model of the patient motion over the respiratory cycle is estimated from the planning 4d ct. the respiratory motion is then computed on-the-fly from this model using a respiratory signal extracted from the cb projections and incorporated into the motion-compensated cbct reconstruction algorithm. the proposed method is evaluated on 26 cbct scans of 3 patients acquired with two protocols used for static and respiration-correlated cbct respectively. our results show that this method provides cbct images within a few seconds after the end of the acquisition where most of the motion artifacts have been removed.
noninvasive functional imaging of volumetric cardiac electrical activity: a human study on myocardial infarction. identification of infarct substrates provides necessary guidance to the prevention and treatment of cardiac arrhythmias. compared to diagnostic criteria of body surface potentials (bsp) or electrophysiological information on heart surfaces, the underlying volumetric cardiac electrical activity is of more direct clinical relevance in exhibiting patient-specific arrhythmic dynamics and arrhythmogenic substrates. we have developed a paradigm for noninvasive imaging of volumetric myocardial transmembrane potential from bsps. in this paper, we present a human study for a patient with acute myocardial infarction. using patient mri and bsp data, the framework is able to reconstruct details of the complete arrhythmic electrical activity on the 3d myocardium of the patient. exploring a subset of the results, the extent, centroid and affected segments of the infarct is correctly evaluated, with comparable performance to existent best results. this human study demonstrates the potential of the presented paradigm as a noninvasive functional imaging technique for patient-specific volumetric cardiac electrical activity in practice.
discovering structure in the space of activation profiles in fmri. we present a method for discovering patterns of activation observed through fmri in experiments with multiple stimuli/tasks. we introduce an explicit parameterization for the profiles of activation and represent fmri time courses as such profiles using linear regression estimates. working in the space of activation profiles, we design a mixture model that finds the major activation patterns along with their localization maps and derive an algorithm for fitting the model to the fmri data. the method enables functional group analysis independent of spatial correspondence among subjects. we validate this model in the context of category selectivity in the visual cortex, demonstrating good agreement with prior findings based on hypothesis-driven methods.
symmetric log-domain diffeomorphic registration: a demons-based approach. modern morphometric studies use non-linear image registration to compare anatomies and perform group analysis. recently, log-euclidean approaches have contributed to promote the use of such computational anatomy tools by permitting simple computations of statistics on a rather large class of invertible spatial transformations. in this work, we propose a non-linear registration algorithm perfectly fit for log-euclidean statistics on diffeomorphisms. our algorithm works completely in the log-domain, i.e. it uses a stationary velocity field. this implies that we guarantee the invertibility of the deformation and have access to the true inverse transformation. this also means that our output can be directly used for log-euclidean statistics without relying on the heavy computation of the log of the spatial transformation. as it is often desirable, our algorithm is symmetric with respect to the order of the input images. furthermore, we use an alternate optimization approach related to thirion's demons algorithm to provide a fast non-linear registration algorithm. first results show that our algorithm outperforms both the demons algorithm and the recently proposed diffeomorphic demons algorithm in terms of accuracy of the transformation while remaining computationally efficient.
shape-based alignment of hippocampal subfields: evaluation in postmortem mri. this paper estimates the accuracy of hippocampal subfield alignment via shape-based normalization. evaluation takes place in postmortem mri dataset acquired at 9.4 tesla with many averages and approximately 0.01,mm3 voxel resolution. continuous medial representations (cm-reps) are used to establish geometrical correspondences between hippocampal formations in different images; the extent to which these correspondences match up subfields is evaluated and compared to normalization driven by image forces. shape-based normalization is shown to perform only slightly worse than image-based normalization; this is encouraging because the former is more applicable to in vivo mri, which typically lacks features that distinguish hippocampal subfields.
spherical demons: fast surface registration. we present the fast spherical demons algorithm for registering two spherical images. by exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently implemented on the sphere using convolution. based on the one parameter subgroups of diffeomorphisms, the resulting registration is diffeomorphic and fast --- registration of two cortical mesh models with more than 100k nodes takes less than 5 minutes, comparable to the fastest surface registration algorithms. moreover, the accuracy of our method compares favorably to the popular freesurfer registration algorithm. we validate the technique in two different settings: (1) parcellation in a set of in-vivo cortical surfaces and (2) brodmann area localization in ex-vivo cortical surfaces.
a discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-d mri. in this paper we present a fully automated approach to the segmentation of pediatric brain tumors in multi-spectral 3-d magnetic resonance images. it is a top-down segmentation approach based on a markov random field (mrf) model that combines probabilistic boosting trees (pbt) and lower-level segmentation via graph cuts. the pbt algorithm provides a strong discriminative observation model that classifies tumor appearance while a spatial prior takes into account the pair-wise homogeneity in terms of classification labels and multi-spectral voxel intensities. the discriminative model relies not only on observed local intensities but also on surrounding context for detecting candidate regions for pathology. a mathematically sound formulation for integrating the two approaches into a unified statistical framework is given. the proposed method is applied to the challenging task of detection and delineation of pediatric brain tumors. this segmentation task is characterized by a high non-uniformity of both the pathology and the surrounding non-pathologic brain tissue. a quantitative evaluation illustrates the robustness of the proposed method. despite dealing with more complicated cases of pediatric brain tumors the results obtained are mostly better than those reported for current state-of-the-art approaches to 3-d mr brain tumor segmentation in adult patients. the entire processing of one multi-spectral data set does not require any user interaction, and takes less time than previously proposed methods.
automatic subcortical segmentation using a contextual model. automatically segmenting subcortical structures in brain images has the potential to greatly accelerate drug trials and population studies of disease. here we propose an automatic subcortical segmentation algorithm using the auto context model. unlike many segmentation algorithms that separately compute a shape prior and an image appearance model, we develop a framework based on machine learning to learn a unified appearance and context model. we trained our algorithm to segment the hippocampus and tested it on 83 brain mris (of 35 alzheimer's disease patients, 22 with mild cognitive impairment, and 26 normal healthy controls). using standard distance and overlap metrics, the auto context model method significantly outperformed simpler learning-based algorithms (using adaboost alone) and the freesurfer system. in tests on a public domain dataset designed to validate segmentation [1] , our new algorithm also greatly improved upon a recently-proposed hybrid discriminative/generative approach [2], which was among the top three that performed comparably in a recent head-to-head competition.
measuring brain lesion progression with a supervised tissue classification system. brain lesions, especially white matter lesions (wmls), are associated with cardiac and vascular disease, but also with normal aging. quantitative analysis of wml in large clinical trials is becoming more and more important. in this paper, we present a computer-assisted wml segmentation method, based on local features extracted from conventional multi-parametric magnetic resonance imaging (mri) sequences. a framework for preprocessing the temporal data by jointly equalizing histograms reduces the spatial and temporal variance of data, thereby improving the longitudinal stability of such measurements and hence the estimate of lesion progression. a support vector machine (svm) classifier trained on expert-defined wml's is applied for lesion segmentation on each scan using the adaboost algorithm. validation on a population of 23 patients from 3 different imaging sites with follow-up studies and wmls of varying sizes, shapes and locations tests the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from an experienced neuroradiologist. the results show that our cad-system achieves consistent lesion segmentation in the 4d data facilitating the disease monitoring.
multi-level classification of emphysema in hrct lung images using delegated classifiers. emphysema is a common chronic respiratory disorder characterized by the destruction of lung tissue. it is a progressive disease where the early stages are characterized by diffuse appearance of small air spaces and later stages exhibit large air spaces called bullae. a bullous region is a sharply demarcated region of emphysema. in this paper, we show that an automated texture-based system based on delegated classifiers is capable of achieving multiple levels of emphysema extraction in high resolution computed tomography (hrct) images. the key idea of delegation is that a cautious classifier makes predictions that meet a minimum level of confidence, and delegates the difficult or uncertain predictions to a more specialized classifier. in this paper, we design a two-step scenario where a first classifier chooses the examples to classify on and delegates the more difficult examples to a second classifier. we compare this technique to well known emphysema classification techniques and ensemble methods such as bagging and boosting. comparison of the results shows that the techniques presented here are more accurate. from a medical standpoint, the classifiers built at different iterations appear to show an interesting correlation with different levels of emphysema.
weights and topology: a study of the effects of graph construction on 3d image segmentation. graph-based algorithms have become increasingly popular for medical image segmentation. the fundamental process for each of these algorithms is to use the image content to generate a set of weights for the graph and then set conditions for an optimal partition of the graph with respect to these weights. to date, the heuristics used for generating the weighted graphs from image intensities have largely been ignored, while the primary focus of attention has been on the details of providing the partitioning conditions. in this paper we empirically study the effects of graph connectivity and weighting function on the quality of the segmentation results. to control for algorithm-specific effects, we employ both the graph cuts and random walker algorithms in our experiments.
modelling childbirth: comparing athlete and non-athlete pelvic floor mechanics. there is preliminary evidence that athletes involved in high-intensity sports for sustained periods have a higher probability of experiencing a prolonged second stage of labour compared to non-athletes. the mechanisms responsible for these differences are not clear, although it is postulated that muscle hypertrophy and increased muscle tone in athletes may contribute to difficulties in vaginal delivery. in order to test these hypotheses, we have constructed individual-specific finite element models of the female pelvic floor (one athlete and one non-athlete) and the fetal head to simulate vaginal delivery and enable quantitative analysis of the differences. the motion of the fetal head descending through the pelvic floor was modelled using finite deformation elasticity with contact mechanics. the force required to push the head was compared between the models and a 45% increase in peak force was observed in the athlete model compared to the non-athlete. in both cases, the overall maximum stretch was induced at the muscle insertions to the pubis. this is the beginning of a quantitative modelling framework that is intended to help clinicians assess the risk of natural versus caesarean birth by taking into account the possible mechanical response of pelvic floor muscles based on their size and activation patterns prior to labour.
texture classification in lung ct using local binary patterns. in this paper we propose to use local binary patterns (lbp) as features in a classification framework for classifying different texture patterns in lung computed tomography. image intensity is included by means of the joint lbp and intensity histogram, and classification is performed using the k nearest neighbor classifier with histogram similarity as distance measure.the proposed method is evaluated on a set of 168 regions of interest comprising normal tissue and different emphysema patterns, and compared to a filter bank based on gaussian derivatives. the joint lbp and intensity histogram, achieving a classification accuracy of 95.2%, shows superior performance to using the common approach of taking moments of the filter response histograms as features, and slightly better performance than using the full filter response histograms instead. classification results are better than some of those previously reported in the literature.
model-based segmentation of hippocampal subfields in ultra-high resolution in vivo mri. recent developments in mr data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. in this paper, we propose a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution mri data. using a bayesian approach, we build a computational model of how images around the hippocampal area are generated, and use this model to obtain automated segmentations. we validate the proposed technique by comparing our segmentation results with corresponding manual delineations in ultra-high resolution mri scans of five individuals.
robust image-based ivus pullbacks gating. intracoronary ultrasound (ivus) imaging allows to obtain high resolution images of internal part of coronary arteries. this tool is unique in the possibility to explore internal vessel structures of the coronary wall, being a powerful tool for diagnosis [1]. since the coronary vessel is moving due to the periodical contraction and expansion of heart muscles, the acquired images present different artifacts. one of the most severe problems is the longitudinal oscillation of the ivus catheter inside the vessel. to alleviate this problem, ecg-gating has been proposed. the goal of gating is to have subsequent frames that represent the internal vessel section in "stable" position and avoid the repetition of frames; that is to generate an image sequence in which the artifacts due to the heart beat have been removed while, possible translation due to vessel tortuosity can still be present. this paper presents a simple and efficient model of catheter longitudinal movement together with a fast and robust image based gating algorithm. experimental results on 9 sequences from 7 patients, plus a comparison with ecg gating are presented.
automatic delineation of sulci and improved partial volume classification for accurate 3d voxel-based cortical thickness estimation from mr. accurate cortical thickness estimation in-vivo is important for the study of many neurodegenerative diseases. when using magnetic resonance images (mri), accuracy may be hampered by artifacts such as partial volume (pv) as the cortex spans only a few voxels. in zones of opposed sulcal banks (tight sulci) the measurement can be even more difficult. the aim of this work is to propose a voxel-based cortical thickness estimation method from mr by integrating a mechanism for correcting sulci delineation after an improved partial volume classification. first, an efficient and accurate framework was developed to enhance partial volume classification with structural information. then, the correction of sulci delineation is performed after a homotopic thinning of a cost function image. integrated to our voxel-based cortical thickness estimation pipeline, the overall method showed a better estimate of thickness and a high reproducibility on real data (r 2 > 0.9). a quantitative analysis on clinical data from an alzheimer's disease study showed significant differences between normal controls and alzheimer's disease patients.
optimal feature point selection and automatic initialization in active shape model search. this paper presents a novel approach for robust and fully automatic segmentation with active shape model search. the proposed method incorporates global geometric constraints during feature point search by using inter-landmark conditional probabilities. the a* graph search algorithm is adapted to identify in the image the optimal set of valid feature points. the technique is extended to enable reliable and fast automatic initialization of the asm search. validation with 2-d and 3-d mr segmentation of the left ventricular epi-cardial border demonstrates significant improvement in robustness and overall accuracy, while eliminating the need for manual initialization.