waashk/dblp huggingface dataset:
inter-subject modelling of liver deformation during radiation therapy. this paper presents a statistical model of the liver deformation that occurs in addition to the quasi-periodic respiratory motion. having an elastic but still compact model of this variability is an important step towards reliable targeting in radiation therapy. to build this model, the deformation of the liver at exhalation was determined for 12 volunteers over roughly one hour using 4dmri and subsequent non-rigid registration. the correspondence between subjects was established based on mechanically relevant landmarks on the liver surface. leave-one-out experiments were performed to evaluate the accuracy in predicting the liver deformation from partial information, such as a point tracked by ultrasound imaging. already predictions from a single point strongly reduced the localisation errors, whilst the method is robust with respect to the exact choice of the measured predictor.
mr-less high dimensional spatial normalization of 11c pib pet images on a population of elderly, mild cognitive impaired and alzheimer disease patients. β¿ amyloid (aβ) plaques are one of the neuropathological hallmarks of alzheimer's disease (ad) and can be quantified using the marker $^{11}\textnormal{c}$ pib. as $^{11}\textnormal{c}$ pib pet images have limited anatomical information, an magnetic resonance image (mri) is usually acquired to perform the spatial normalization needed for population analysis. we designed and evaluated a high dimensional spatial normalization approach that only uses the $^{11}\textnormal{c}$ pib pet image. the non-rigid registration (nrr) is based on free form deformation (ffd) modelled using b-splines. to compensate for the limited anatomical information, the ffd is constrained to an allowable transform space using a model trained from mr registrations. aβ deposition is dependent on disease staging, so a spatially normalized $^{11}\textnormal{c}$ pib pet appearance model selects and refines the atlas. the approach was compared with mr nrr using data from healthy elderly, mild cognitive impaired and alzheimer disease participants. using segmentation propagation, an average dice similarity coefficient of 0.64 and 0.73 was obtained for white and gray matter. the r-squared correlation between the uptake obtained in the frontal, parietal, occipital and temporal was 0.789, 0.843, 0.871 and 0.964. these are very promising results, considering the low resolution of $^{11}\textnormal{c}$ pib pet images.
fully automatic segmentation of the hippocampus and the amygdala from mri using hybrid prior knowledge. the segmentation of macroscopically ill-defined and highly variable structures, such as the hippocampus hc and the amygdala am, from mri requires specific constraints. here, we describe and evaluate a hybrid segmentation method that uses knowledge derived from a probabilistic atlas and from anatomical landmarks based on stable anatomical characteristics of the structures. combined in a previously published semi-automatic segmentation method, they lead to a fast, robust and accurate fully automatic segmentation of hc and am. the probabilistic atlas was built from 16 young controls and registered with the "unified segmentation" of spm5. the algorithm was quantitatively evaluated with respect to manual segmentation on two mri datasets: the 16 young controls, with a leave-one-out strategy, and a mixed cohort of 8 controls and 15 subjects with epilepsy with variable hippocampal sclerosis. the segmentation driven by hybrid knowledge leads to greatly improved results compared to that obtained by registration of the thresholded atlas alone: mean overlap for hc on the 16 young controls increased from 78% to 87% (p < 0.001) and on the mixed cohort from 73% to 82% (p < 0.001) while the error on volumes decreased from 10% to 7% (p < 0.005) and from 18% to 8% (p < 0.001), respectively. automatic results were better than the semi-automatic results: for the 16 young controls, average overlap increased from 84% to 87% (p < 0.001) for hc and from 81% to 84% (p < 0.002) for am, with equivalent improvements in volume error.
clinical neonatal brain mri segmentation using adaptive nonparametric data models and intensity-based markov priors. this paper presents a bayesian framework for neonatal brain-tissue segmentation in clinical magnetic resonance (mr) images. this is a challenging task because of the low contrast-to-noise ratio and large variance in both tissue intensities and brain structures, as well as imaging artifacts and partial-volume effects in clinical neonatal scanning. we propose to incorporate a spatially adaptive likelihood model using a data-driven nonparametric statistical technique. the method initially learns an intensity-based prior, relying on the empirical markov statistics from training data, using fuzzy nonlinear support vector machines (svm). in an iterative scheme, the models adapt to spatial variations of image intensities via nonparametric density estimation. the method is effective even in the absence of anatomical atlas priors. the implementation, however, can naturally incorporate probabilistic atlas priors and markov-smoothness priors to impose additional regularity on segmentation. the maximum-a-posteriori (map) segmentation is obtained within a graph-cut framework. cross validation on clinical neonatal brain-mr images demonstrates the efficacy of the proposed method, both qualitatively and quantitatively.
exploratory identification of image-based biomarkers for solid mass pulmonary tumors. if imaging is to serve as a valid biomarker in the assessment of the response of cancer to therapies, a reproducible and predictive radiologic metric is required. a biomarker is an indicator of a biological property that can be used to measure the progress of disease. while current size-based, quantitative techniques provide numerical representations of tumors, they are not necessarily indicative of disease progression for advanced cancers. in this paper, we present an end-to-end process to explore the use of other image-based features especially statistical textural features for cancer change detection. we exploit the earth mover's distance metric for measuring the change in the tumor burden over a period, between the time the baseline scans were taken, and the time the therapy response scans were taken. the time-to-progression (ttp) of the disease is our known patient outcome. we analyze the correlations between ttp and our change measurements and discover that the local texture energy feature is most predictive of disease progression, more so than the tumor burden size on which current quantitative measures are made.
modeling glioma growth and mass effect in 3d mr images of the brain. in this article, we propose a framework for modeling glioma growth and the subsequent mechanical impact on the surrounding brain tissue (mass-effect) in a medical imaging context. glioma growth is modeled via nonlinear reaction-advection-diffusion, with a two-way coupling with the underlying tissue elastic deformation. tumor bulk and infiltration and subsequent mass-effects are not regarded separately, but captured by the model itself in the course of its evolution. our formulation is fully eulerian and naturally allows for updating the tumor diffusion coefficient following structural displacements caused by tumor growth/infiltration. we show that model parameters can be estimated via optimization based on imaging data, using efficient solution algorithms on regular grids. we test the model and the automatic optimization framework on real brain tumor data sets, achieving significant improvement in landmark prediction compared to a simplified purely mechanical approach.
active-contour-based image segmentation using machine learning techniques. we introduce a non-linear shape prior for the deformable model framework that we learn from a set of shape samples using recent manifold learning techniques. we model a category of shapes as a finite dimensional manifold which we approximate using diffusion maps. our method computes a delaunay triangulation of the reduced space, considered as euclidean, and uses the resulting space partition to identify the closest neighbors of any given shape based on its nyström extension. we derive a non-linear shape prior term designed to attract a shape towards the shape prior manifold at given constant embedding. results on shapes of ventricle nuclei demonstrate the potential of our method for segmentation tasks.
automatic recovery of the left ventricular blood pool in cardiac cine mr images. we present a method for automatic localization and rough segmentation of the left ventricle blood pool in cardiac cine magnetic resonance images. the method first detects the whole heart using time-based fourier analysis. it then segments the left ventricle blood pool by grouping connected components across slices using isoperimetric clustering. the system was tested on 253 datasets and failed in only 2 cases.
towards tracking breast cancer across medical images using subject-specific biomechanical models. breast cancer detection, diagnosis and treatment increasingly involves images of the breast taken with different degrees of breast deformation. we introduce a new biomechanical modelling framework for predicting breast deformation and thus aiding the combination of information derived from the various images. in this paper, we focus on mr images of the breast under different loading conditions, and consider methods to map information between the images. we generate subject-specific finite element models of the breast by semi-automatically fitting geometrical models to segmented data from breast mr images, and characterizing the subject-specific mechanical properties of the breast tissues. we identified the unloaded reference configuration of the breast by acquiring mr images of the breast under neutral buoyancy (immersed in water). such imaging is clearly not practical in the clinical setting, however this previously unavailable data provides us with important data with which to validate models of breast biomechanics, and provides a common configuration with which to refer and interpret all breast images. we demonstrate our modelling framework using a pilot study that was conducted to assess the mechanical performance of a subject-specific homogeneous biomechanical model in predicting deformations of the breast of a volunteer in a prone gravity-loaded configuration. the model captured the gross characteristics of the breast deformation with an rms error of 4.2 mm in predicting the skin surface of the gravity-loaded shape, which included tissue displacements of over 20 mm. internal tissue features identified from the mr images were tracked from the reference state to the prone gravity-loaded configuration with a mean error of 3.7 mm. we consider the modelling assumptions and discuss how the framework could be refined in order to further improve the tissue tracking accuracy.
de-enhancing the dynamic contrast-enhanced breast mri for robust registration. dynamic enhancement causes serious problems for registration of contrast enhanced breast mri, due to variable uptakes of agent on different tissues or even same tissues in the breast. we present an iterative optimization algorithm to de-enhance the dynamic contrast-enhanced breast mri and then register them for avoiding the effects of enhancement on image registration. in particular, the spatially varying enhancements are modeled by a markov random field, and estimated by a locally smooth function with boundaries using a graph cut algorithm. the de-enhanced images are then registered by conventional b-spline based registration algorithm. these two steps benefit from each other and are repeated until the results converge. experimental results show that our two-step registration algorithm performs much better than conventional mutual information based registration algorithm. also, the effects of tumor shrinking in the conventional registration algorithms can be effectively avoided by our registration algorithm.
brain fiber architecture, genetics, and intelligence: a high angular resolution diffusion imaging (hardi) study. we developed an analysis pipeline enabling population studies of hardi data, and applied it to map genetic influences on fiber architecture in 90 twin subjects. we applied tensor-driven 3d fluid registration to hardi, resampling the spherical fiber orientation distribution functions (odfs) in appropriate riemannian manifolds, after odf regularization and sharpening. fitting structural equation models (sem) from quantitative genetics, we evaluated genetic influences on the jensen-shannon divergence (jsd), a novel measure of fiber spatial coherence, and on the generalized fiber anisotropy (gfa; [1]) a measure of fiber integrity. with random-effects regression, we mapped regions where diffusion profiles were highly correlated with subjects' intelligence quotient (iq). fiber complexity was predominantly under genetic control, and higher in more highly anisotropic regions; the proportion of genetic versus environmental control varied spatially. our methods show promise for discovering genes affecting fiber connectivity in the brain.
simulation of ground-truth validation data via physically- and statistically-based warps. the problem of scarcity of ground-truth expert delineations of medical image data is a serious one that impedes the training and validation of medical image analysis techniques. we develop an algorithm for the automatic generation of large databases of annotated images from a single reference dataset. we provide a web-based interface through which the users can upload a reference data set (an image and its corresponding segmentation and landmark points), provide custom setting of parameters, and, following server-side computations, generate and download an arbitrary number of novel ground-truth data, including segmentations, displacement vector fields, intensity non-uniformity maps, and point correspondences. to produce realistic simulated data, we use variational (statistically-based) and vibrational (physically-based) spatial deformations, nonlinear radiometric warps mimicking imaging non-homogeneity, and additive random noise with different underlying distributions. we outline the algorithmic details, present sample results, and provide the web address to readers for immediate evaluation and usage.
effects of registration regularization and atlas sharpness on segmentation accuracy. in this paper, we propose a unified framework for computing atlases from manually labeled data at various degrees of "sharpness" and the joint registration-segmentation of a new brain with these atlases. in non-rigid registration, the tradeoff between warp regularization and image fidelity is typically set empirically. in segmentation, this leads to a probabilistic atlas of arbitrary "sharpness": weak regularization results in well-aligned training images and a "sharp" atlas; strong regularization yields a "blurry" atlas. we study the effects of this tradeoff in the context of cortical surface parcellation by comparing three special cases of our framework, namely: progressive registration-segmentation of a new brain to increasingly "sharp" atlases with increasingly flexible warps; secondly, progressive registration to a single atlas with increasingly flexible warps; and thirdly, registration to a single atlas with fixed constrained warps. the optimal parcellation in all three cases corresponds to a unique balance of atlas "sharpness" and warp regularization that yield statistically significant improvements over the previously demonstrated parcellation results.
conformal slit mapping and its applications to brain surface parameterization. we propose a method that computes a conformal mapping from a multiply connected mesh to the so-called slit domain, which consists of a canonical rectangle or disk in which 3d curved landmarks on the original surfaces are mapped to concentric or parallel lines in the slit domain. in this paper, we studied its application to brain surface parameterization. after cutting along some landmark curve features on surface models of the cerebral cortex, we obtain multiple connected domains. by computing exact harmonic one-forms, closed harmonic one-forms, and holomorphic one-forms, we are able to build a circular slit mapping that conformally maps the surface to an annulus with some concentric arcs and a rectangle with some slits. the whole algorithm is based on solving linear systems so it is very stable. in the slit domain parameterization results, the feature curves are either mapped to straight lines or concentric arcs. this representation is convenient for anatomical visualization, and may assist statistical comparisons of anatomy, surface-based registration and signal processing. preliminary experimental results parameterizing various brain anatomical surfaces are presented.
attenuation resilient aif estimation based on hierarchical bayesian modelling for first pass myocardial perfusion mri. non-linear attenuation of the arterial input function (aif) is a major problemin first-pass mr perfusion imaging due to the high concentration of the contrast agent in the blood pool. this paper presents a technique to reconstruct the true aif using signal intensities in the myocardium and the attenuated aif based on a hierarchical bayesian model (hbm). with the proposed method, both the aif and the response function are modeled as smoothed functions by using bayesian penalty splines (p-splines). the derived aif is then used to estimate the impulse response of the myocardium based on deconvolution analysis. the proposed technique is validated both with simulated data using the mmid4 model and ten in vivo data sets for estimating myocardial perfusion reserve rates. the results demonstrate the ability of the proposed technique in accurately reconstructing the desired aif for myocardial perfusion quantification. the method does not involve any mri pulse sequence modification, and thus is expected to have wider clinical impact.
automatic target and trajectory identification for deep brain stimulation (dbs) procedures. this paper presents an automatic surgical target and trajectory identification technique for planning deep brain stimulation (dbs) procedures. the probabilistic functional maps, constructed from population-based actual stimulating field information and intra-operative electrophysiological activities, were integrated into a neurosurgical visualization and navigation system to facilitate the surgical planning and guidance. in our preliminary studies, we compared the actual surgical target locations and trajectories established by an experienced stereotactic neurosurgeon with those automatically planned using our probabilistic functional maps on 10 subthalamic nucleus (stn) dbs procedures. the average displacement between the surgical target locations in both groups was 1.82mm with a standard deviation of 0.77mm. the difference between the surgical trajectories was 3.1° and 2.3° in the lateral-to-medial and anterior-to-posterior orientations respectively.
modeling of needle-tissue interaction using ultrasound-based motion estimation. a needle-tissue interaction model is an essential part of every needle insertion simulator. in this paper, a new experimental method for the modeling of needle-tissue interaction is presented. the method consists of measuring needle and tissue displacements with ultrasound, measuring needle base forces, and using a deformation simulation model to identify the parameters of a needle-tissue interaction model. the feasibility of this non-invasive approach was demonstrated in an experiment in which a brachytherapy needle was inserted into a prostate phantom. ultrasound radio-frequency data and the time-domain cross-correlation method, often used in ultrasound elastography, were used to generate the tissue displacement field during needle insertion. a three-parameter force density model was assumed for the needle-tissue interaction. with the needle displacement, tissue displacement and needle base forces as input data, finite element simulations were carried out to adjust the model parameters to achieve a good fit between simulated and measured data.
methods for inverting dense displacement fields: evaluation in brain image registration. in medical image analysis there is frequently a need to invert dense displacement fields which map one image space to another. in this paper we describe inversion techniques and determine their accuracy in the context of 18 inter-subject brain image registrations. scattered data interpolation (sdi) is used to initialise locally and globally consistent iterative techniques. the inverse-consistency error, eic is computed over the whole image and over 10 specific brain regions. sdi produced good results with mean (max) eic ∼ 0.02mm (2.0mm). both iterative method produced mean errors of ∼ 0.005mm but the globally consistent method resulted in a smaller maximum error (1.9mm compared with 1.4mm). the largest errors were in the cerebral cortex with large outlier errors in the ventricles. simple iterative techniques are, on this evidence, able to produce reasonable estimates of inverse displacement fields provided there is good initialisation.
hierarchical shape statistical model for segmentation of lung fields in chest radiographs. the standard active shape model (asm) generally uses a whole population to train a single pca-based shape model for segmentation of all testing samples. since some testing samples can be similar to only sub-population of training samples, it will be more effective if particular shape statistics extracted from the respective sub-population can be used for guiding image segmentation. accordingly, we design a set of hierarchical shape statistical models, including a whole-population shape model and a series of sub-population models. the whole-population shape model is used to guide the initial segmentation of the testing sample, and the initial segmentation result is then used to select a suitable sub-population shape model according to the shape similarity between the testing sample and each sub-population. by using the selected sub-population shape model, the segmentation result can be further refined. to achieve this segmentation process, several particular steps are designed next. first, all linearly aligned samples in the whole population are used to generate a whole-population shape model. second, an affinity propagation method is used to cluster all linearly aligned samples into several clusters, to determine the samples belonging to the same sub-populations. third, the original samples of each sub-population are linearly aligned to their own mean shape, and the respective sub-population shape model is built using the newly aligned samples in this sub-population. by using all these three steps, we can generate hierarchical shape statistical models to guide image segmentation. experimental results show that the proposed method can significantly improve the segmentation performance, compared to conventional asm.
real-time modeling of vascular flow for angiography simulation. interventional neuroradiology is a growing field of minimally invasive therapies that includes embolization of aneurysms and arteriovenous malformations, carotid angioplasty and carotid stenting, and acute stroke therapy. treatment is performed using image-guided instrument navigation through the patient's vasculature and requires intricate combination of visual and tactile coordination. in this paper we present a series of techniques for real-time high-fidelity simulation of angiographic studies. we focus in particular on the computation and visualization of blood flow and blood pressure distribution patterns, mixing of blood and contrast agent, and high-fidelity simulation of fluoroscopic images.