waashk/dblp huggingface dataset:
efficient computation of pdf-based characteristics from diffusion mr signal. we present a general method for the computation of pdf-based characteristics of the tissue micro-architecture in mr imaging. the approach relies on the approximation of the mr signal by a series expansion based on spherical harmonics and laguerre-gaussian functions, followed by a simple projection step that is efficiently done in a finite dimensional space. the resulting algorithm is generic, flexible and is able to compute a large set of useful characteristics of the local tissues structure. we illustrate the effectiveness of this approach by showing results on synthetic and real mr datasets acquired in a clinical time-frame.
computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients. renal cell carcinoma (rcc) can be diagnosed by histological tissue analysis where exact counts of cancerous cell nuclei are required. we propose a completely automated image analysis pipeline to predict the survival of rcc patients based on the analysis of immunohistochemical staining of mib-1 on tissue microarrays. a random forest classifier detects cell nuclei of cancerous cells and predicts their staining. the classifier training is achieved by expert annotations of 2300 nuclei gathered from tissues of 9 different rcc patients. the application to a test set of 133 patients clearly demonstrates that our computational pathology analysis matches the prognostic performance of expert pathologists.
constitutive modeling of human liver based on in vivo measurements. in vivo aspiration experiments on human livers are analyzed and material parameters for a non-linear-viscoelastic constitutive model are determined. a novel procedure is applied for the inverse analysis that accounts for the initial tissue deformation in the experiment and for the non-homogeneity of liver tissue. a numerical model is used consisting of a surface layer (capsule) and an underlying non-linear-viscoelastic solid (parenchyma). the capsule is modeled as hyperelastic membrane using data from measurements on bovine and human tissue. in a two step optimization procedure the set of constitutive model parameters for the "average" response of liver parenchyma is obtained. the proposed model is in line with literature values of high strain rate elastic modulus obtained from dynamic elastography. the model can be used to predict the nonlinear, time dependent behavior of human liver in computer simulations related to surgery training and planning.
exploring the use of proper orthogonal decomposition for enhancing blood flow images via computational fluid dynamics. obtaining high quality patient-specific flow velocity information is not an easy task. available clinical data are usually poorly resolved and contain a significant amount of noise. we propose a novel approach to integrate computational fluid dynamics with measurement data to overcome this difficulty. by performing a proper orthogonal decomposition of simulated blood flow patterns for a given vascular location with various anatomical configurations it is possible to obtain a basis model for flow reconstruction. this is used to interpolate imaging data intelligently without having to perform a full flow simulation for each individual patient. this work focuses on assessing the feasibility of such a method.
real-time nonlinear fem with neural network for simulating soft organ model deformation. this paper presents a new method for simulating the deformation of organ models by using a neural network. the proposed method is based on the idea proposed by chen et al. [2] that a deformed model can be estimated from the superposition of basic deformation modes. the neural network finds a relationship between external forces and the models deformed by the forces. the experimental results show that the trained network can achieve a real-time simulation while keeping the acceptable accuracy compared with the nonlinear fem computation.
impact of rician adapted non-local means filtering on hardi. in this paper we study the impact of denoising the raw high angular resolution diffusion imaging (hardi) data with the non-local means filter adapted to rician noise (nlmr). we first show that nlmr filtering improves robustness of apparent diffusion coefficient (adc) and orientation distribution function (odf) reconstructions from synthetic hardi datasets. our results suggest that the nlmr filtering improve the quality of anisotropy maps computed from adc and odf and improve the coherence of q-ball odfs with the underlying anatomy while not degrading angular resolution. these results are shown on a biological phantom with known ground truth and on a real human brain dataset. most importantly, we show that multiple measurements of diffusion-weighted (dw) images and averaging these images along each direction can be avoided because nlmr filtering of the individual dw images produces better quality generalized fractional anisotropy maps and more accurate odf fields than when computed from the averaged dw datasets.
detection of deformable objects in 3d images using markov-chain monte carlo and spherical harmonics. we address the problem of segmenting 3d microscopic volumetric intensity images of a collection of spatially correlated objects (such as fluorescently labeled nuclei in a tissue). this problem arises in the study of tissue morphogenesis where cells and cellular components are organized in accord with biological role and fate. we formulate the image model as stochastically generated based on biological priors and physics of image formation. we express the segmentation problem in terms of bayesian inference and use data-driven markov chain monte carlo to fit the image model to data. we perform an initial step in which the intensity volume is approximated as an expansion in 4d spherical harmonics, the coefficients of which capture the general organization of objects. since cell nuclei are membrane-bound their shapes are subject to membrane lipid bilayer bending energy, which we use to constrain individual contours. moreover, we parameterize the nuclear contours using spherical harmonic functions, which provide a shape description with no restriction to particular symmetries. we demonstrate the utility of our approach using synthetic and real fluorescence microscopy data.
patch-based markov models for event detection in fluorescence bioimaging. the study of protein dynamics is essential for understanding the multi-molecular complexes at subcellular levels. fluorescent protein (xfp)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells, unraveling the live states of the matter. original image analysis methods are then required to process challenging 2d or 3d image sequences. recently, tracking methods that estimate the whole trajectories of moving objects have been successfully developed. in this paper, we address rather the detection of meaningful events in spatio-temporal fluorescence image sequences, such as apparent stable "stocking areas" involved in membrane transport. we propose an original patch-based markov modeling to detect spatial irregularities in fluorescence images with low false alarm rates. this approach has been developed for real image sequences of cells expressing xfp-tagged rab proteins, known to regulate membrane trafficking.
a local mutual information guided denoising technique and its application to self-calibrated partially parallel imaging. the application of partially parallel imaging (ppi) techniques to regular clinical magnetic resonance imaging (mri) studies has brought about the benefit of significantly faster acquisitions but at the cost of amplified and spatially variant noise, especially, for high parallel imaging acceleration rates. a local mutual information (lmi) weighted total variation (tv) based model is proposed to remove non-evenly distributed noise while preserving image sharpness. for self-calibrated ppi, such as generalized auto-calibration partially parallel acquisition (grappa) and modified sensitivity encoding (msense), a low spatial resolution high signal to noise ratio (snr) image is available besides the reconstructed high spatial resolution low snr image. the lmi between these two images is used to detect the noise distribution and the location of edges automatically, and is then applied as guidance for denoising. to better preserve sharpness, bregman iteration scheme is utilized to add the removed signal back to the denoised image. entropy of the residual map is used to automatically terminate iteration without using any information of the golden standard or real noise. results of the proposed algorithm on synthetic and in vivo mr images indicate that the proposed technique preserves image edges and suppresses noise well in the images reconstructed by grappa. the comparison with some existing techniques further confirms the advantages. this algorithm can be applied to enhance the clinical applicability of self-calibrated ppi. potentially, it can be extended to denoise general images with spatially variant noise.
rician noise removal by non-local means filtering for low signal-to-noise ratio mri: applications to dt-mri. diffusion-weighted mri (dw-mri) is subject to random noise yielding measures that are different from their real values, and thus biasing the subsequently estimated tensors. the non-local means (nlmeans) filter has recently been proposed to denoise mri with high signal-to-noise ratio (snr). this filter has been shown to allow the best restoration of image intensities for the estimation of diffusion tensors (dt) compared to state-of-the-art methods. however, for dw-mr images with high b-values (and thus low snr), the noise, which is strictly rician-distributed, can no longer be approximated as additive white gaussian, as implicitly assumed in the classical formulation of the nlmeans. high b-values are typically used in high angular resolution diffusion imaging (hardi) or q-space imaging (qsi), for which an optimal restoration is critical. in this paper, we propose to adapt the nlmeans filter to rician noise corrupted data. validation is performed on synthetic data and on real data for both conventional mr images and dt images. our adaptation outperforms the original nlmeans filter in terms of peak-signal-to-noise ratio (psnr) for dw-mri.
towards regional elastography of intracranial aneurysms. weak spots in the aneurysm could be identified estimating the regional stiffness of the wall. our approach consists in defining a parametric biomechanical model of the vessel which, given the patient's vascular morphology and the blood in- and outflow obtained from non-invasive imaging as well as parameters describing the local elasticity of the wall, enables the computation of the theoretical deformed wall position. the distance between this latter and the one obtained from the aneurysm pulsation is iteratively minimized in order to estimate the optimal set of stiffness parameters. in order to reduce the number of variables to estimate, the aneurysm morphology is clustered into a limited number of regions with uniform stiffness. a random noise perturbation (<5mm) is applied to the reference deformations and strains, showing that the robustness of the clustering decreases to 75% and errors of the stiffness estimates remain below 10% of the reference values.
gaze-contingent motor channelling and haptic constraints for minimally invasive robotic surgery. the use of master-slave surgical robots for minimally invasive surgery (mis) has created a physical separation between the surgeon and the patient. reconnecting the essential visuomotor sensory feedback is important for the safe practice of robotic assisted mis procedures. this paper introduces a novel gaze contingent framework with real-time haptic feedback by transforming visual sensory information into physical constraints that can interact with the motor sensory channel. we demonstrate how motor tracking of deforming tissue can be made more effective and accurate through the concept of gaze-contingent motor channelling. the method also uses 3d eye gaze to dynamically prescribe and update safety boundaries during robotic assisted mis without prior knowledge of the soft-tissue morphology. initial validation results on both simulated and robotic assisted phantom procedures demonstrate the potential clinical value of the technique.
registration of 4d time-series of cardiac images with multichannel diffeomorphic demons. in this paper, we propose a generic framework for inter-subject non-linear registration of 4d time-series images. in this framework, spatio-temporal registration is defined by mapping trajectories of physical points as opposed to spatial registration that solely aims at mapping homologous points. first, we determine the trajectories we want to register in each sequence using a motion tracking algorithm based on the diffeomorphic demons algorithm. then, we perform simultaneously pairwise registrations of corresponding time-points with the constraint to map the same physical points over time. we show this trajectory registration can be formulated as a multichannel registration of 3d images. we solve it using the diffeomorphic demons algorithm extended to vector-valued 3d images. this framework is applied to the inter-subject non-linear registration of 4d cardiac ct sequences.
tag separation in cardiac tagged mri. in this paper we introduce a tag separation method for better cardiac boundary segmentation and tag tracking. our approach is based on two observations in the cardiac tagged mr images: 1) the tag patterns have a regular texture; 2) the cardiac images without tag patterns are piecewise smooth with sparse gradients. these observations motivate us to use two dictionaries, one based on the discrete cosine transform for representing tag patterns and the other based on the wavelet transform for representing the underlying cardiac image without tag patterns. the two dictionaries are built such that they can lead to sparse representations of the tag patterns and of the piece-wise smooth regions without tag patterns. with the two dictionaries, a new tag separation approach is proposed to simultaneously optimize w.r.t. the two sparse representations, where optimization is directed by the total variation regularization scheme. while previous methods have focused on tag removal, our approach to acquiring both optimally-decomposed tag-only image and the cardiac image without tags simultaneously can be used for better tag tracking and cardiac boundary segmentation. we demonstrate the superior performance of the proposed approach through extensive experiments on large sets of cardiac tagged mr images.
automatic guidance of an ultrasound probe by visual servoing based on b-mode image moments. we propose a new visual servo approach to automatically control in real-time the full motion of a 2d ultrasound (us) probe held by a medical robot in order to reach a desired image of motionless soft tissue object in b-mode ultrasound imaging. combinations of image moments of the observed object cross-section are used as feedback information in the visual control scheme. these visual features are extracted in real-time from the us image thanks to a fast image segmentation method. simulations performed with a static us volume containing an egg-shaped object, and ex-vivo experiments using a robotized us probe that interacts with a motionless rabbit heart immersed in water, show the validity of this new approach and its robustness to different perturbations. this method shows promise for a variety of us-guided medical interventions that require real-time servoing.
efficient 3d tracking for motion compensation in beating heart surgery. the design of physiological motion compensation systems for robotic-assisted cardiac minimally invasive surgery (mis) is a challenging research topic. in this domain, vision-based techniques have proven to be a practical way to retrieve the motion of the beating heart. however due to the complexity of the heart motion and its surface characteristics, efficient tracking is still a complicated task. in this paper, we propose an algorithm for tracking the 3d motion of the beating heart, based on a thin-plate splines (tps) parametric model. the novelty of our approach lies in that no explicit matching between the stereo camera images is required and consequently no intermediate steps such as rectification are needed. experiments conducted on ex-vivo and in-vivo tissue show the effectiveness of the proposed algorithm for tracking surfaces undergoing complex deformations.
how does the camera assistant decide the zooming ratio of laparoscopic images? analysis and implementation. an important factor for defining a good image during laparoscopic surgery is the zooming ratio, which corresponds to the depth of insertion of the laparoscope along its longitudinal axis. however, it is not clear how surgeons (camera assistants) decide the zooming ratio of laparoscopic images during surgery. conventional automatic camera positioning systems define the zooming ratio "uniformly" based on simple heuristics. however, because the most adequate zooming ratio varies widely during surgery, these conventional systems may not offer the specific view that the surgeon wants. therefore, we first investigated how the camera assistant decides the zooming ratio of laparoscopic images by fully analyzing the positional relationship between the laparoscope and the surgical instrument during laparoscopic surgery. then, we extracted the zooming behavior and implemented it in the robotic laparoscope positioner that we previously developed. as a result, the zooming behavior of our robotic system became very similar to that of the human camera assistant. it was found that the proposed zooming motion of our robotic system may be suitable for fast and compact operations during surgery.
deformable mosaicing for whole-body mri. whole-body magnetic resonance imaging is an emerging application gaining vast clinical interest during the last years. although recent technological advances shortened the longish acquisition time, this is still the limiting factor avoiding its wide-spread clinical usage. the acquisition of images with large field-of-view helps to relieve this drawback, but leads to significantly distorted images. therefore, we propose a deformable mosaicing approach, based on the simultaneous registration to linear weighted averages, to correct for distortions in the overlapping area. this method produces good results on in-vivo data and has the advantage that a seamless integration into the clinical workflow is possible.
consensus-locally linear embedding (c-lle): application to prostate cancer detection on magnetic resonance spectroscopy. locally linear embedding (lle) is a widely used non-linear dimensionality reduction (nldr) method that projects multi-dimensional data into a low-dimensional embedding space while attempting to preserve object adjacencies from the original high-dimensional feature space. a limitation of lle, however, is the presence of free parameters, changing the values of which may dramatically change the low dimensional representations of the data. in this paper, we present a novel consensus-lle (c-lle) scheme which constructs a stable consensus embedding from across multiple low dimensional unstable lle data representations obtained by varying the parameter (&kappa;) controlling locally linearity. the approach is analogous to breiman's bagging algorithm for generating ensemble classifiers by combining multiple weak predictors into a single predictor. in this paper we demonstrate the utility of c-lle in creating a low dimensional stable representation of magnetic resonance spectroscopy (mrs) data for identifying prostate cancer. results of quantitative evaluation demonstrate that our c-lle scheme has higher cancer detection sensitivity (86.90%) and specificity (85.14%) compared to lle and other state of the art schemes currently employed for analysis of mrs data.
bayesian motion recovery framework for myocardial phase-contrast velocity mri. detailed assessment of myocardial motion provides a key indicator of ventricular function, enabling the early detection and assessment of a range of cardiac abnormalities. existing techniques for myocardial contractility analysis are complicated by a combination of factors including resolution, acquisition time, and consistency of quantification results. phase-contrast velocity mri is a technique that provides instantaneous, in vivo measurement of tissue velocity on a per-voxel basis. it allows for the direct derivation of contractile indices with minimal post-processing. for this method to be clinically useful, snr and image artifacts need to be addressed. the purpose of this paper is to present a maximum a posteriori (map) restoration technique for high quality myocardial motion recovery. it employs an accurate noise modeling scheme and a generalized gaussian markov random field prior tailored for the myocardial morphology. the quality of the proposed method is evaluated with both simulated myocardial velocity data with known ground truth and in vivo phase-contrast mr velocity acquisitions from a group of normal subjects.
autogate: fast and automatic doppler gate localization in b-mode echocardiogram. in this paper, we propose an algorithm for fast and automatic doppler gate localization in spectral doppler echocardiography using the b-mode image information. the algorithm has two components: 1) cardiac standard view classification and 2) gate location inference. for cardiac view classification, we incorporate the probabilistic boosting network (pbn) principle to local-structure-dependent object classification, which speeds up the processing time as it breaks down the computational dependency on the number of classes. the gate location is computed using a data-driven shape inference approach. clinical evaluation was performed by implementing the algorithm on an ultrasound system. experiment results show that the performance of the proposed algorithm is comparable to the doppler gate placement by an expert user. to the best of our knowledge, this is the first algorithm that provides a real time solution to the automated doppler gate placement in the clinical environment.
a variational level set approach to segmentation and bias correction of images with intensity inhomogeneity. this paper presents a variational level set approach to joint segmentation and bias correction of images with intensity inhomogeneity. our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the intensity inhomogeneity. we first define a weighted k-means clustering objective function for image intensities in a neighborhood around each point, with the cluster centers having a multiplicative factor that estimates the bias within the neighborhood. the objective function is then integrated over the entire domain and incorporated into a variational level set formulation. the energy minimization is performed via a level set evolution process. our method is able to estimate bias of quite general profiles. moreover, it is robust to initialization, and therefore allows automatic applications. the proposed method has been used for images of various modalities with promising results.