waashk/dblp huggingface dataset:
quantification of blood flow from rotational angiography. for assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3d) information on vessel morphology and hemodynamics. rotational angiography is routinely used to determine the 3d geometry and we propose a method to exploit the same acquisition to determine the blood flow waveform and the mean volumetric flow rate. the method uses a model of contrast agent dispersion to determine the flow parameters from the spatial and temporal development of the contrast agent concentration, represented by a flow map. furthermore, it also overcomes artifacts due to the rotation of the c-arm using a newly introduced reliability map. the method was validated on images from a computer simulation and from a phantom experiment. with a mean error of 11.0% for the mean volumetric flow rate and 15.3% for the blood flow waveform from the phantom experiments, we conclude that the method has the potential to give quantitative estimates of blood flow parameters during cerebrovascular interventions.
on simulating subjective evaluation using combined objective metrics for validation of 3d tumor segmentation. in this paper, we present a new segmentation evaluation method that can simulate radiologist's subjective assessment of 3d tumor segmentation in ct images. the method uses a new metric defined as a linear combination of a set of commonly used objective metrics. the weighing parameters of the linear combination are determined by maximizing the rank correlation between radiologist's subjective rating and objective measurements. experimental results on 93 lesions demonstrate that the new composite metric shows better performance in segmentation evaluation than each individual objective metric. also, segmentation rating using the composite metric compares well with radiologist's subjective evaluation. our method has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale segmentation evaluation studies.
fuzzy nonparametric dti segmentation for robust cingulum-tract extraction. this paper presents a novel segmentation-based approach for fiber-tract extraction in diffusion-tensor (dt) images. typical tractography methods, incorporating thresholds on fractional anisotropy and fiber curvature to terminate tracking, can face serious problems arising from partial voluming and noise. for these reasons, tractography often fails to extract thin tracts with sharp changes in orientation, e.g. the cingulum. unlike tractography--which disregards the information in the tensors that were previously tracked--the proposed method extracts the cingulum by exploiting the statistical coherence of tensors in the entire structure. moreover, the proposed segmentation-based method allows fuzzy class memberships to optimally extract information within partial-volumed voxels. unlike typical fuzzy-segmentation schemes employing gaussian models that are biased towards ellipsoidal clusters, the proposed method models the manifolds underlying the classes by incorporating nonparametric data-driven statistical models. furthermore, it exploits the nonparametric model to capture the spatial continuity and structure of the fiber bundle. the results on real dt images demonstrate that the proposed method extracts the cingulum bundle significantly more accurately as compared to tractography.
classification of suspected liver metastases using fmri images: a machine learning approach. this paper presents a machine-learning approach to the interactive classification of suspected liver metastases in fmri images. the method uses fmri-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. changes in hepatic hemodynamics are evaluated from $t_2^*$-w fmri images acquired during the breathing of air, air-co 2, and carbogen. a classification model is build to differentiate between tumors and healthy liver tissues. to validate our method, a model was built from 29 mice datasets, and used to classify suspicious regions in 16 new datasets of healthy subjects or subjects with metastases in earlier growth phases. our experimental results on mice yielded an accuracy of 78% with high precision (88%). this suggests that the method can provide a useful aid for early detection of liver metastases.
registration of cardiac spect/ct data through weighted intensity co-occurrence priors. the introduction of hybrid scanners has greatly increased the popularity of molecular imaging techniques. many clinical applications benefit from combining complementary information based on the precise alignment of the two modalities. in case the alignment is inaccurate, then this crucial assumption often made for subsequent processing steps will be violated. however, this violation may not be apparent to the physician. in ct-based attenuation correction (ac) for cardiac spect/ct data, critical misalignments between spect and ct can lead to spurious perfusion defects. in this work, we focus on increasing the accuracy of rigid volume registration of cardiac spect/ct data by using prior knowledge. a new weighting scheme for an intensity co-occurrence prior is introduced to assure accurate and robust alignment in the local heart region. experimental results demonstrate that the proposed method out-performs mutual information registration and shows robustness across a selection of learned distributions acquired from 15 different patients.
detection and segmentation of pathological structures by the extended graph-shifts algorithm. we propose an extended graph-shifts algorithm for image segmentation and labeling. this algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. it consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. extended graph-shifts can be applied to a broad range of problems in medical imaging. in this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. the energy terms in these tasks are learned from training data by statistical learning algorithms.we demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3d volume in about one minute.
real-time mr diffusion tensor and q-ball imaging using kalman filtering. magnetic resonance diffusion imaging (dmri) has become an established research tool for the investigation of tissue structure and orientation. in this paper, we present a method for real time processing of diffusion tensor and q-ball imaging. the basic idea is to use kalman filtering framework to fit either the linear tensor or q-ball model. because the kalman filter is designed to be an incremental algorithm, it naturally enables updating the model estimate after the acquisition of any new diffusion-weighted volume. processing diffusion models and maps during ongoing scans provides a new useful tool for clinicians, especially when it is not possible to predict how long a subject may remain still in the magnet.
left ventricle tracking using overlap priors. this study investigates overlap priors for tracking the left ventricle (lv) endo- and epicardium boundaries in cardiac magnetic resonance (mr) sequences. it consists of evolving two curves following the euler-lagrange minimization of two functionals each containing an original overlap prior constraint. the latter measures the conformity of the overlap between the nonparametric (kernel-based) intensity distributions within the three target regions---lv cavity, myocardium and background---to a prior learned from a given segmentation of the first frame. the bhattacharyya coefficient is used as an overlap measure. different from existing intensity-driven constraints, the overlap priors do not assume implicitly that the overlap between the distributions within different regions has to be minimal. although neither shape priors nor curve coupling were used, quantitative evaluation showed that the results correlate well with independent manual segmentations and the method compares favorably with other recent methods. the overlap priors lead to a lv tracking which is more versatile than existing methods because the solution is not bounded to the shape/intensity characteristics of a training set. we also demonstrate experimentally that the used overlap measures are approximately constant over a cardiac sequence.
real-time nonlinear finite element analysis for surgical simulation using graphics processing units. clinical employment of biomechanical modelling techniques in areas of medical image analysis and surgical simulation is often hindered by conflicting requirements for high fidelity in the modelling approach and high solution speeds. we report the development of techniques for high-speed nonlinear finite element (fe) analysis for surgical simulation. we employ a previously developed nonlinear total lagrangian explicit fe formulation which offers significant computational advantages for soft tissue simulation. however, the key contribution of the work is the presentation of a fast graphics processing unit (gpu) solution scheme for the fe equations. to the best of our knowledge this represents the first gpu implementation of a nonlinear fe solver. we show that the present explicit fe scheme is well-suited to solution via highly parallel graphics hardware, and that even a midrange gpu allows significant solution speed gains (up to 16.4×) compared with equivalent cpu implementations. for the models tested the scheme allows real-time solution of models with up to 16000 tetrahedral elements. the use of gpus for such purposes offers a cost-effective high-performance alternative to expensive multi-cpu machines, and may have important applications in medical image analysis and surgical simulation.
modelling intravasation of liquid distension media in surgical simulators. we simulate the intravasation of liquid distention media into the systemic circulation as it occurs during hysteroscopy and transurethral resection of the prostate. a linear network flow model is extended with a correction for non-newtonian blood behaviour in small vessels and an appropriate handling of vessel compliance. we then integrate a fast lookup scheme in order to allow for real-time simulation. cutting of tissue is accounted for by adjusting pressure boundary conditions for all cut vessels. we investigate the influence of changing distention fluid pressure settings and of the position of tissue cuts. our simulation predicts significant intravasation only on the venous side, and just in cases when larger veins are cut. the implemented methods allow the realistic control of bleeding for short-term and the total resulting intravasation volume for long-term complication scenarios. while the simulation is fast enough to support real-time training, it is also adequate for explaining intravasation effects which were previously observed on a phenomenological level only.
nonrigid registration of dynamic renal mr images using a saliency based mrf model. nonrigid registration of contrast-enhanced mr images is a difficult problem due to the change in pixel intensity caused by the wash-in and wash-out of the contrast agent. in this paper we propose a novel saliency based markov random field approach for effective nonrigid registration of contrast enhanced images. saliency information obtained from the neurobiology-based saliency model alongwith intensity information is used to quantify the degree of similarity between images in the pre- and post-contrast stages. information from these two features is combined by using an exponential function of the saliency difference such that it assigns low values to small differences in saliency and at the same time ensures that saliency information does not bias the energy term. rotationally-invariant edge information from edge-orientation histograms was used to complement the saliency information resulting in better registration results. tests on real patient datasets show that our algorithm results in accurate registration. we also simulated elastic motion on images, and the deformation field recovered by our algorithm was nearly the inverse of the simulated field.
3d cerebral cortical morphometry in autism: increased folding in children and adolescents in frontal, parietal, and temporal lobes. this paper presents a systematic evaluation of cortical folding, or complexity, in autism. it introduces two novel measures to analyze folding in a specific region of interest, which, unlike traditional measures, produce an intuitive easily-interpretable description of folding and inform the nature of folding change by incorporating local surface-patch orientation. this study reports new findings of increased cortical folding in autistics in the frontal, parietal, and temporal lobes, as compared to controls. these differences are stronger in children than adolescents. the paper validates part of the findings using the new measures based on comparisons with traditional measures. unlike studies in the literature, this paper reports new findings, via a fully 3d folding analysis on all brain lobes, based on the consensus of virtually all 6 folding measures used (2 new, 4 traditional) via rigorous statistical permutation testing. in these ways, this paper not only strengthens some previous clinical findings, but also extends the state of the art in autism research.
measuring brain variability via sulcal lines registration: a diffeomorphic approach. in this paper we present a new way of measuring brain variability based on the registration of sulcal lines sets in the large deformation framework. lines are modelled geometrically as currents, avoiding then matchings based on point correspondences. at the end we retrieve a globally consistent deformation of the underlying brain space that best matches the lines. thanks to this framework the measured variability is defined everywhere whereas a previous method introduced by p. fillard requires tensors extrapolation. evaluating both methods on the same database, we show that our new approach enables to describe different details of the variability and to highlight the major trends of deformation in the database thanks to a tangent-pca analysis.
robust autonomous model learning from 2d and 3d data sets. in this paper we propose a weakly supervised learning algorithm for appearance models based on the minimum description length (mdl) principle. from a set of training images or volumes depicting examples of an anatomical structure, correspondences for a set of landmarks are established by group-wise registration. the approach does not require any annotation. in contrast to existing methods no assumptions about the topology of the data are made, and the topology can change throughout the data set. instead of a continuous representation of the volumes or images, only sparse finite sets of interest points are used to represent the examples during optimization. this enables the algorithm to efficiently use distinctive points, and to handle texture variations robustly. in contrast to standard elasticity based deformation constraints the mdl criterion accounts for systematic deformations typical for training sets stemming from medical image data. experimental results are reported for five different 2d and 3d data sets.
robust vessel tree modeling. in this paper, we present a novel method for extracting center axis representations (centerlines) of blood vessels in contrast enhanced (ce)-cta/mra, robustly and accurately. this graph-based optimization algorithm which employs multi-scale medialness filters extracts vessel centerlines by computing the minimum-cost paths. specifically, first, new medialness filters are designed from the assumption of circular/elliptic vessel cross-sections. these filters produce contrast and scale independent responses even the presence of nearby structures. second, they are incorporated to the minimum-cost path detection algorithm in a novel way for the computational efficiency and accuracy. third, the full vessel centerline tree is constructed from this optimization technique by assigning a saliency measure for each centerline from their length and radius information. the proposed method is computationally efficient and produces results that are comparable in quality to the ones created by experts. it has been tested on more than 100 coronary artery data set where the full coronary artery trees are extracted in 21 seconds in average on a 3.2ghz pc.
active scheduling of organ detection and segmentation in whole-body medical images. with the advance of whole-body medical imaging technologies, computer aided detection/diagnosis (cad) is being scaled up to deal with multiple organs or anatomical structures simultaneously. multiple tasks (organ detection/segmentation) in a cad system are often highly dependent due to the anatomical context within a human body. in this paper, we propose a method to schedule multi-organ detection/segmentation based on information theory. the central idea is to schedule tasks in an order that each operation achieves maximum expected information gain. the scheduling rule is formulated to embed two intuitive principles: (1) a task with higher confidence tends to be scheduled earlier; (2) a task with higher predictive power for other tasks tends to be scheduled earlier. more specifically, task dependency is modeled by conditional probability; the outcome of each task is assumed to be probabilistic as well; and the scheduling criterion is based on the reduction of the summed conditional entropy over all tasks. the validation is carried out on two challenging cad problems, multi-organ detection in whole-body ct and liver segmentation in pet-ct. compared to unscheduled and ad hoc scheduled organ detection/segmentation, our scheduled execution achieves higher accuracy with faster speed.
characterizing task-related temporal dynamics of spatial activation distributions in fmri bold signals. we present a new functional magnetic resonance imaging (fmri) analysis method that incorporates both spatial and temporal dynamics of blood-oxygen-level dependent (bold) signals within a region of interest (roi). 3d moment descriptors are used to characterize the spatial changes in bold signals over time. the method is tested on fmri data collected from eight healthy subjects performing a bulb-squeezing motor task with their right-hand at various frequencies. multiple brain regions including the left cerebellum, both primary motor cortices (m1), both supplementary motor areas (sma), left prefrontal cortex (pfc), and left anterior cingulate cortex (acc) demonstrate significant task-related changes. furthermore, our method is able to discriminate differences in activation patterns at the various task frequencies, whereas using a traditional intensity based method, no significant activation difference is detected. this suggests that temporal dynamics of the spatial distribution of bold signal provide additional information regarding task-related activation thus complementing conventional intensity-based approaches.
contraction detection in small bowel from an image sequence of wireless capsule endoscopy. this paper describes a method for automatic detection of contractions in the small bowel through analyzing wireless capsule endoscopic images. based on the characteristics of contraction images, a coherent procedure that includes analyzes of the temporal and spatial features is proposed. for temporal features, the image sequence is examined to detect candidate contractions through the changing number of edges and an evaluation of similarities between the frames of each possible contraction to eliminate cases of low probability. for spatial features, descriptions of the directions at the edge pixels are used to determine contractions utilizing a classification method. the experimental results show the effectiveness of our method that can detect a total of 83% of cases. thus, this is a feasible method for developing tools to assist in diagnostic procedures in the small bowel.
boundary-specific cost functions for quantitative airway analysis. computed tomography (ct) images of the lungs provide high resolution views of the airways. quantitative measurements such as lumen diameter and wall thickness help diagnose and localize airway diseases, assist in surgical planning, and determine progress of treatment. automated quantitative analysis of such images is needed due to the number of airways per patient. we present an approach involving dynamic programming coupled with boundary-specific cost functions that is capable of differentiating inner and outer borders. the method allows for precise delineation of the inner lumen and outer wall. the results are demonstrated on synthetic data, evaluated on human datasets compared to human operators, and verified on phantom ct scans to sub-voxel accuracy.
how do registration parameters affect quantitation of lung kinematics? assessing the quality of motion estimation in the lung remains challenging. we approach the problem by imaging isolated porcine lungs within an artificial thorax with four-dimensional computed tomography (4dct). respiratory kinematics are estimated via pairwise non-rigid registration using different metrics and image resolutions. landmarks are manually identified on the images and used to assess accuracy by comparing known displacements to the registration-derived displacements. we find that motion quantitation becomes less precise as the inflation interval between images increases. in addition, its sensitivity to image resolution varies anatomically. mutual information and cross-correlation perform similarly, while mean squares is significantly poorer. however, none of the metrics compensate for the difficulty of registering over a large inflation interval. we intend to use the results of these experiments to more effectively and efficiently quantify pulmonary kinematics in future, and to explore additional parameter combinations.
atlas-based segmentation of the germinal matrix from in utero clinical mri of the fetal brain. recently developed techniques for reconstruction of high-resolution 3d images from fetal mr scans allows us to study the morphometry of developing brain tissues in utero. however, existing adult brain analysis methods cannot be directly applied as the anatomy of the fetal brain is significantly different in terms of geometry and tissue morphology. we describe an approach to atlas-based segmentation of the fetal brain with particular focus on the delineation of the germinal matrix, a transient structure related to brain growth. we segment 3d images reconstructed from in utero clinical mr scans and measure volumes of different brain tissue classes for a group of fetal subjects at gestational age 20.5---22.5 weeks. we also include a partial validation of the approach using manual tracing of the germinal matrix at different gestational ages.
nonlinear registration of diffusion mr images based on fiber bundles. in this paper, we explore the use of fiber bundles extracted from diffusion mr images for a nonlinear registration algorithm. we employ a white matter atlas to automatically label major fiber bundles and to establish correspondence between subjects. we propose a polyaffine framework to calculate a smooth and invertible nonlinear warp field based on these correspondences, and derive an analytical solution for the reorientation of the tensor fields under the polyaffine transformation. we demonstrate our algorithm on a group of subjects and show that it performs comparable to a higher dimensional nonrigid registration algorithm.