waashk/dblp huggingface dataset:
closed-loop control in fused mr-trus image-guided prostate biopsy. multi-modality fusion imaging for targeted prostate biopsy is difficult because of prostate motion during the biopsy procedure. a closed-loop control mechanism is proposed to improve the efficacy and safety of the biopsy procedure, which uses real-time ultrasound and spatial tracking as feedback to adjust the registration between a preoperative 3d image (e.g. mri) and real-time ultrasound images. the spatial tracking data is used to initialize the image-based registration between intraoperative ultrasound images and a preoperative ultrasound volume. the preoperative ultrasound volume is obtained using a 2d sweep and manually registered to the mri dataset before the biopsy procedure. the accuracy of the system is 2.3±0.9 mm in phantom studies. the results of twelve patient studies show that prostate motion can be effectively compensated using closed-loop control.
medical and technical protocol for automatic navigation of a wireless device in the carotid artery of a living swine using a standard clinical mri system. a 1.5 mm magnetic sphere was navigated automatically inside the carotid artery of a living swine. the propulsion force, tracking and real-time capabilities of a magnetic resonance imaging (mri) system were integrated into a closed loop control platform. the sphere was released using an endovascular catheter approach. specially developed software is responsible for the tracking, propulsion, event timing and closed loop position control in order to follow a 10 roundtrips preplanned trajectory on a distance of 5 cm inside the right carotid artery of the animal. experimental protocol linking the technical aspects of this in vivo assay is presented. in the context of this demonstration, many challenges which provide insights about concrete issues of future nanomedical interventions and interventional platforms have been identified and addressed.
improving the contrast of breast cancer masses in ultrasound using an autoregressive model based filter. the assessment and diagnosis of breast cancer with ultrasound is a challenging problem due to the low contrast between cancer masses and benign tissue. due to this low contrast it has proven to be difficult to achieve reliable segmentation results on breast cancer masses. an autoregressive model has been employed to filter out of the backscattered rf-signal from a tissue harmonic image which is not degraded by harmonic leakage. measurements on the filtered image have shown a significant (up to 45 %) increase in contrast between cancer masses and benign tissue.
outlier rejection for diffusion weighted imaging. this paper introduces an outlier rejection and signal reconstruction method for high angular resolution diffusion weighted imaging. the approach is based on the thresholding of laplacian measurements over the sphere of the apparent diffusion coefficient profiles defined for a given set of gradient directions. exemplary results are presented.
generating fiber crossing phantoms out of experimental dwis. in diffusion tensor imaging (dti), differently oriented fiber bundles inside one voxel are incorrectly modeled by a single tensor. high angular resolution diffusion imaging (hardi) aims at using more complex models, such as a two-tensor model, for estimating two fiber bundles. we propose a new method for creating experimental phantom data of fiber crossings, by mixing the dwi-signals from high fa-regions with different orientation. the properties of these experimental phantoms approach the conditions of real data. these phantoms can thus serve as a 'ground truth' in validating crossing reconstruction algorithms. the angular resolution of a dual tensor model is determined using series of crossings, generated under different angles. an angular resolution of 0.6π was found in data scanned with a diffusion weighting parameter b=1000 s/mm2. this resolution did not change significantly in experiments with b=3000 and 5000 s/mm2, keeping the scanning time constant.
motion and positional error correction for cone beam 3d-reconstruction with mobile c-arms. ct-images acquired by mobile c-arm devices can contain artefacts caused by positioning errors. we propose a data driven method based on iterative 3d-reconstruction and 2d/3d-registration to correct projection data inconsistencies. with a 2d/3d-registration algorithm, transformations are computed to align the acquired projection images to a previously reconstructed volume. in an iterative procedure, the reconstruction algorithm uses the results of the registration step. this algorithm also reduces small motion artefacts within 3d-reconstructions. experiments with simulated projections from real patient data show the feasibility of the proposed method. in addition, experiments with real projection data acquired with an experimental robotised c-arm device have been performed with promising results.
cortical hemisphere registration via large deformation diffeomorphic metric curve mapping. we present large deformation diffeomorphic metric curve mapping (lddmm-curve) for registering cortical hemispheres. we showed global cortical hemisphere matching and evaluated the mapping accuracy in five subregions of the cortex in fourteen mri scans.
tagged volume rendering of the heart. we present a novel system for 3-d visualisation of the heart and coronary arteries. binary tags (generated offline) are combined with value-gradient transfer functions (specified online) allowing for interactive visualisation, while relaxing the offline segmentation criteria. the arteries are roughly segmented using a hessian-based line filter and the pericardial cavity using a fast marching active contour. a comparison of different contour initialisations reveals that simple geometric shapes (such as spheres or extruded polygons) produce suitable results.
one-class acoustic characterization applied to blood detection in ivus. intravascular ultrasound (ivus) is an invasive imaging modality capable of providing cross-sectional images of the interior of a blood vessel in real time and at normal video framerates (10-30 frames/s). low contrast between the features of interest in the ivus imagery remains a confounding factor in ivus analysis; it would be beneficial therefore to have a method capable of detecting certain physical features imaged under ivus in an automated manner. we present such a method and apply it to the detection of blood. while blood detection algorithms are not new in this field, we deviate from traditional approaches to ivus signal characterization in our use of 1-class learning. this eliminates certain problems surrounding the need to provide "foreground" and "background" (or, more generally, n-class) samples to a learner. applied to the blood-detection problem on 40 mhz recordings made in vivo in swine, we are able to achieve ∼95% sensitivity with ∼90% specificity at a radial resolution of ∼600 µm.
phase sensitive reconstruction for water/fat separation in mr imaging using inverse gradient. this paper presents a novel method for phase unwrapping for phase sensitive reconstruction in mr imaging. the unwrapped phase is obtained by integrating the phase gradient by solving a poisson equation. an efficient solver, which has been made publicly available, is used to solve the equation. the proposed method is demonstrated on a fat quantification mri task that is a part of a prospective study of fat accumulation. the method is compared to a phase unwrapping method based on region growing. results indicate that the proposed method provides more robust unwrapping. unlike region growing methods, the proposed method is also straight-forward to implement in 3d.
locus: local cooperative unified segmentation of mri brain scans. we propose to carry out cooperatively both tissue and structure segmentations by distributing a set of local and cooperative models in a unified mrf framework. tissue segmentation is performed by partitionning the volume into subvolumes where local mrfs are estimated in cooperation with their neighbors to ensure consistency. local estimation fits precisely to the local intensity distribution and thus handles nonuniformity of intensity without any bias field modelization. structure segmentation is performed via local mrfs that integrate localization constraints provided by a priori general fuzzy description of brain anatomy. structure segmentation is not reduced to a postprocessing step but cooperates with tissue segmentation to gradually and conjointly improve models accuracy. the evaluation was performed using phantoms and real 3t brain scans. it shows good results and in particular robustness to nonuniformity and noise with a low computational cost.
spline based inhomogeneity correction for c-pib pet segmentation using expectation maximization. with the advent of biomarkers such as 11c-pib and the increase in use of pet, automated methods are required for processing and analyzing datasets from research studies and in clinical settings. a common preprocessing step is the calculation of standardized uptake value ratio (suvr) for inter-subject normalization. this requires segmented grey matter (gm) for voi refinement. however 11c-pib uptake is proportional to amyloid build up leading to inhomogeneities in intensities, especially within gm. inhomogeneities present a challenge for clustering and pattern classification based approaches to pet segmentation as proposed in current literature. in this paper we modify a mr image segmentation technique based on expectation maximization for 11c-pib pet segmentation. a priori probability maps of the tissue types are used to initialize and enforce anatomical constraints. we developed a bézier spline based inhomogeneity correction techniques that is embedded in the segmentation algorithm and minimizes inhomogeneity resulting in better segmentations of 11c-pib pet images. we compare our inhomogeneity with a global polynomial correction technique and validate our approach using co-registered mri segmentations.
hyperspherical von mises-fisher mixture (hvmf) modelling of high angular resolution diffusion mri. a mapping of unit vectors onto a 5d hypersphere is used to model and partition odfs from hardi data. this mapping has a number of useful and interesting properties and we make a link to interpretation of the second order spherical harmonic decompositions of hardi data. the paper presents the working theory and experiments of using a von mises-fisher mixture model for directional samples. the mle of the second moment of the hvmf pdf can also be related to fractional anisotropy. we perform error analysis of the estimation scheme in single and multi-fibre regions and then show how a penalised-likelihood model selection method can be employed to differentiate single and multiple fibre regions.
use of varying constraints in optimal 3-d graph search for segmentation of macular optical coherence tomography images. an optimal 3-d graph search approach designed for simultaneous multiple surface detection is extended to allow for varying smoothness and surface interaction constraints instead of the traditionally used constant constraints. we apply the method to the intraretinal layer segmentation of 24 3-d optical coherence tomography (oct) images, learning the constraints from examples in a leave-one-subject-out fashion. introducing the varying constraints decreased the mean unsigned border positioning errors (mean error of 7.3 ± 3.7 µm using varying constraints compared to 8.3 ± 4.9 µm using constant constraints and 8.2 ± 3.5 µm for the inter-observer variability).
automatic segmentation of bladder and prostate using coupled 3d deformable models. in this paper, we propose a fully automatic method for the coupled 3d localization and segmentation of lower abdomen structures. we apply it to the joint segmentation of the prostate and bladder in a database of ct scans of the lower abdomen of male patients. a flexible approach on the bladder allows the process to easily adapt to high shape variation and to intensity inhomogeneities that would be hard to characterize (due, for example, to the level of contrast agent that is present). on the other hand, a statistical shape prior is enforced on the prostate. we also propose an adaptive non-overlapping constraint that arbitrates the evolution of both structures based on the availability of strong image data at their common boundary. the method has been tested on a database of 16 volumetric images, and the validation process includes an assessment of inter-expert variability in prostate delineation, with promising results.
characterizing spatio-temporal patterns for disease discrimination in cardiac echo videos. disease-specific understanding of echocardiographic sequences requires accurate characterization of spatio-temporal motion patterns. in this paper we present a method of automatic extraction and matching of spatio-temporal patterns from cardiac echo videos. specifically, we extract cardiac regions (chambers and walls) using a variation of multiscale normalized cuts that combines motion estimates from deformable models with image intensity. we then derive spatio-temporal trajectories of region measurements such as wall motion, volume and thickness. the region trajectories are then matched to infer the similarities in disease labels of patients. validation results on patient data sets collected from many hospitals are presented.
integrating functional and structural images for simultaneous cardiac segmentation and deformation recovery. because of their physiological meaningfulness, cardiac physiome models have been used as constraints to recover patient information from medical images. although the results are promising, the parameters of the physiome models are not patient-specific, and thus affect the clinical relevance of the recovered information especially in pathological cases. in view of this problem, we incorporate patient information from body surface potential maps in the physiome model to provide a more patient-specific while physiological plausible guidance, which is further coupled with patient measurements derived from structural images to recover the cardiac geometry and deformation simultaneously. experiments have been conducted on synthetic data to show the benefits of the framework, and on real human data to show its practical potential.
statistical shape modeling using mdl incorporating shape, appearance, and expert knowledge. we propose a highly automated approach to the point correspondence problem for anatomical shapes in medical images. manual landmarking is performed on a small subset of the shapes in the study, and a machine learning approach is used to elucidate the characteristic shape and appearance features at each landmark. a classifier trained using these features defines a cost function that drives key landmarks to anatomically meaningful locations after mdl-based correspondence establishment. results are shown for artificial examples as well as real data.
false positive reduction in mammographic mass detection using local binary patterns. in this paper we propose a new approach for false positive reduction in the field of mammographic mass detection. the goal is to distinguish between the true recognized masses and the ones which actually are normal parenchyma. our proposal is based on local binary patterns (lbp) for representing salient micro-patterns and preserving at the same time the spatial structure of the masses. once the descriptors are extracted, support vector machines (svm) are used for classifying the detected masses. we test our proposal using a set of 1792 suspicious regions of interest extracted from the ddsm database. exhaustive experiments illustrate that lbp features are effective and efficient for false positive reduction even at different mass sizes, a critical aspect in mass detection systems. moreover, we compare our proposal with current methods showing that lbp obtains better performance.
automatic labeling of anatomical structures in mr fastview images using a statistical atlas. we present a method for fast and automatic labeling of anatomical structures in mr fastview localizer images, which can be useful for automatic mr examination planning. fastview is a modern mr protocol, that provides larger planning fields of view than previously available with isotropic 3d resolution by scanning during continuous movement of the patient table. hence, full 3d information is obtained within short acquisition time. anatomical labeling is done by registering the images to a statistical atlas created from training image data beforehand. the statistical atlas consists of a statistical model of deformation and a statistical model of grey value appearance. it is generated by non-rigid registration and principal component analysis of the resulting deformation fields and registered images. labeling of an unseen fastview image is done by non-rigid registration of the image to the statistical atlas and propagating the labels from the atlas to the image. in our implementation, the statistical models of deformation and appearance are both implemented on the gpu (graphics processing unit), which permits computing the atlas based labeling using gpu hardware acceleration. the running times of about 10 to 30 seconds are of the same magnitude as the image acquisition itself, which allows for practical usage in clinical mr routine.
adaptive metamorphs model for 3d medical image segmentation. in this paper, we introduce an adaptive model-based segmentation framework, in which edge and region information are integrated and used adaptively while a solid model deforms toward the object boundary. our 3d segmentation method stems from metamorphs deformable models [1]. the main novelty of our work is in that, instead of performing segmentation in an entire 3d volume, we propose model-based segmentation in an adaptively changing subvolume of interest. the subvolume is determined based on appearance statistics of the evolving object model, and within the subvolume, more accurate and object-specific edge and region information can be obtained. this local and adaptive scheme for computing edges and object region information makes our segmentation solution more efficient and more robust to image noise, artifacts and intensity inhomogeneity. external forces for model deformation are derived in a variational framework that consists of both edge-based and region-based energy terms, taking into account the adaptively changing environment. we demonstrate the performance of our method through extensive experiments using cardiac mr and liver ct images.
mixtures of gaussians on tensor fields for dt-mri segmentation. in this paper, an original approach for the segmentation of tensor fields is proposed. based on the modeling of the data by means of gaussian mixtures directly in the tensor domain, this technique presents a wide range of applications in medical image processing, particularly for diffusion tensor magnetic resonance imaging (dt-mri). the performance of the segmentation method proposed is shown through the segmentation of the corpus callosum from a dataset of 32 dt-mri volumes. comparison with a recent and related segmentation approach is favorable to our method, showing its capability for the automatic extraction of anatomical structures in the white matter.
soft level set coupling for lv segmentation in gated perfusion spect. we present a new segmentation approach for the myocardium in gated and non-gated perfusion spect images. to this end, we represent the epi- and endocardiumby separate signed distance functions and couple them by a soft constraint to give explicit control over the wall thickness. by an explicit modeling of the basal plane, the volume of the blood pool as well as the myocardium are determinable. furthermore, prior shape information is incorporated by applying a kernel density estimation on a number of expert segmentations in a low-dimensional pca subspace. thereby, information along the time axis is fully taken into account by employing 4-dimensional embedding functions.
nonrigid image registration with subdivision lattices: application to cardiac mr image analysis. in this paper we present a new methodology for cardiac motion tracking in tagged mri using nonrigid image registration based on subdivision surfaces and subdivision lattices. we use two sets of registrations to do the motion tracking. first, a set of surface registrations is used to create and initially align the subdivision model of the left ventricle with short-axis and long-axis mr images. second, a series of volumetric registrations are used to perform the motion tracking and to reconstruct the 4d cardiac motion field from the tagged mr images. the motion of a point in the myocardium over time is calculated by registering the images taken during systole to the set of reference images taken at end-diastole. registration is achieved by optimizing the positions of the vertices in the base lattice so that the mutual information of the images being registered is maximized. the presented method is validated using a cardiac motion simulator and we also present strain measurements obtained from a group of normal volunteers.
spatio-temporal registration of real time 3d ultrasound to cardiovascular mr sequences. we extend our static multimodal nonrigid registration [1] to a spatio-temporal (2d+t) co-registration of a real-time 3d ultrasound and a cardiovascular mr sequence. the motivation for our research is to assist a clinician to automatically fuse the information from multiple imaging modalities for the early diagnosis and therapy of cardiac disease. the deformation field between both sequences is decoupled into spatial and temporal components. temporal alignment is firstly performed to re-slice both sequences using a differential registration method. spatial alignment is then carried out between the frames corresponding to the same temporal position. the spatial deformation is modeled by the polyaffine transformation whose anchor points (or control points) are automatically detected and refined by calculating a local mis-match measure based on phase mutual information. the spatial alignment is built in an adaptive multi-scale framework to maximize the phase-based similarity measure by optimizing the parameters of the polyaffine transformation. results demonstrate that this novel method can yield an accurate registration to particular cardiac regions.
graph cuts framework for kidney segmentation with prior shape constraints. we propose a novel kidney segmentation approach based on the graph cuts technique. the proposed approach depends on both image appearance and shape information. shape information is gathered from a set of training shapes. then we estimate the shape variations using a new distance probabilistic model which approximates the marginal densities of the kidney and its background in the variability region using a poisson distribution refined by positive and negative gaussian components. to segment a kidney slice, we align it with the training slices so we can use the distance probabilistic model. then its gray level is approximated with a lcg with sign-alternate components. the spatial interaction between the neighboring pixels is identified using a new analytical approach. finally, we formulate a new energy function using both image appearance models and shape constraints. this function is globally minimized using s/t graph cuts to get the optimal segmentation. experimental results show that the proposed technique gives promising results compared to others without shape constraints.