waashk/dblp huggingface dataset:
generalized surface flows for deformable registration and cortical matching. despite being routinely required in medical applications, deformable surface registration is notoriously difficult due to large intersubject variability and complex geometry of most medical datasets.we present a general and flexible deformable matching framework based on generalized surface flows that efficiently tackles these issues through tailored deformation priors and multiresolution computations. the value of our approach over existing methods is demonstrated for automatic and user-guided cortical registration.
lv motion and strain computation from tmri based on meshless deformable models. we propose a novel meshless deformable model for in vivo left ventricle (lv) 3d motion estimation and analysis based on tagged mri (tmri). the meshless deformable model can capture global deformations such as contraction and torsion with a few parameters, while track local deformations with laplacian representation. in particular, the model performs well even when the control points (tag intersections) are relatively sparse. we test the performance of the meshless model on a numeric phantom, as well as in vivo heart data of healthy subjects and patients. the experimental results show that the meshless deformable model can fully recover the myocardial motion and strain in 3d.
statistical atlases of bone anatomy: construction, iterative improvement and validation. we present an iterative bootstrapping framework to create and analyze statistical atlases of bony anatomy such as the human pelvis from a large collection of ct data sets. we create an initial tetrahedral mesh representation of the target anatomy and use deformable intensity-based registration to create an initial atlas. this atlas is used as prior information to assist in deformable registration/segmentation of our subject image data sets, and the process is iterated several times to remove any bias from the initial choice of template subject and to improve the stability and consistency of mean shape and variational modes. we also present a framework to validate the statistical models. using this method, we have created a statistical atlas of full pelvis anatomy with 110 healthy patient ct scans. our analysis shows that any given pelvis shape can be approximated up to an average accuracy of 1.5036 mm using the first 15 principal modes of variation. although a particular intensity-based deformable registration algorithm was used to produce these results, we believe that the basic method may be adapted readily for use with any registration method with broadly similar characteristics.
thoracic ct-pet registration using a 3d breathing model. in the context of thoracic ct-pet volume registration, we present a novel method to incorporate a breathing model in a non-linear registration procedure, guaranteeing physiologically plausible deformations. the approach also accounts for the rigid motions of lung tumors during breathing. we performed a set of registration experiments on one healthy and four pathological data sets. initial results demonstrate the interest of this method to significantly improve the accuracy of multimodal volume registration for diagnosis and radiotherapy applications.
comparison and evaluation of segmentation techniques for subcortical structures in brain mri. the automation of segmentation of medical images is an active research area. however, there has been criticism of the standard of evaluation of methods. we have comprehensively evaluated four novel methods of automatically segmenting subcortical structures using volumetric, spatial overlap and distance-based measures. two of the methods are atlas-based --- classifier fusion and labelling (cfl) and expectation-maximisation segmentation using a dynamic brain atlas (ems), and two model-based --- profile active appearance models (pam) and bayesian appearance models (bam). each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. our results showed that all four methods perform on par with recently published methods. cfl performed significantly better than the other three methods according to all three classes of metrics.
magneto-optic tracking of a flexible laparoscopic ultrasound transducer for laparoscope augmentation. in abdominal surgery, a laparoscopic ultrasound transducer is commonly used to detect lesions such as metastases. the determination and visualization of position and orientation of its flexible tip in relation to the patient or other surgical instruments can be of much help to (novice) surgeons utilizing the transducer intraoperatively. this difficult subject has recently been paid attention to by the scientific community [1, 2, 3, 4, 5, 6]. electromagnetic tracking systems can be applied to track the flexible tip. however, the magnetic field can be distorted by ferromagnetic material. this paper presents a new method based on optical tracking of the laparoscope and magneto-optic tracking of the transducer, which is able to automatically detect field distortions. this is used for a smooth augmentation of the b-scan images of the transducer directly on the camera images in real time.
groupwise combined segmentation and registration for atlas construction. the creation of average anatomical atlases has been a growing area of research in recent years. it is of increased value to construct representations of, not only intensity atlases, but also their segmentation into required tissues or structures. this paper presents novel groupwise combined segmentation and registration approaches, which aim to simultaneously improve both the alignment of intensity images to their average shape, as well as the segmentations of structures in the average space. an iterative em framework is used to build average 3d mr atlases of populations for which prior atlases do not currently exist: preterm infants at one- and two-years old. these have been used to quantify the growth of tissues occurring between these ages.
multivariate normalization with symmetric diffeomorphisms for multivariate studies. current clinical and research neuroimaging protocols acquire images using multiple modalities, for instance, t1, t2, diffusion tensor and cerebral blood flow magnetic resonance images (mri). these multivariate datasets provide unique and often complementary anatomical and physiological information about the subject of interest. we present a method that uses fused multiple modality (scalar and tensor) datasets to perform intersubject spatial normalization. our multivariate approach has the potential to eliminate inconsistencies that occur when normalization is performed on each modality separately. furthermore, the multivariate approach uses a much richer anatomical and physiological image signature to infer image correspondences and perform multivariate statistical tests. in this initial study, we develop the theory for multivariate symmetric normalization (mvsyn), establish its feasibility and discuss preliminary results on a multivariate statistical study of 22q deletion syndrome.
cell spreading analysis with directed edge profile-guided level set active contours. cell adhesion and spreading within the extracellular matrix (ecm) plays an important role in cell motility, cell growth and tissue organization. measuring cell spreading dynamics enables the investigation of cell mechanosensitivity to external mechanical stimuli, such as substrate rigidity. a common approach to measure cell spreading dynamics is to take time lapse images and quantify cell size and perimeter as a function of time. in our experiments, differences in cell characteristics between different treatments are subtle and require accurate measurements of cell parameters across a large population of cells to ensure an adequate sample size for statistical hypothesis testing. this paper presents a new approach to estimate accurate cell boundaries with complex shapes by applying a modified geodesic active contour level set method that directly utilizes the halo effect typically seen in phase contrast microscopy. contour evolution is guided by edge profiles in a perpendicular direction to ensure convergence to the correct cell boundary. the proposed approach is tested on bovine aortic endothelial cell images under different treatments, and demonstrates accurate segmentation for a wide range of cell sizes and shapes compared to manual ground truth.
robust kernel methods for sparse mr image reconstruction. a major challenge in contemporary magnetic resonance imaging (mri) lies in providing the highest resolution exam possible in the shortest acquisition period. recently, several authors have proposed the use of l1-norm minimization for the reconstruction of sparse mr images fromhighly-undersampled k-space data. despite promising results demonstrating the ability to accurately reconstruct images sampled at rates significantly below the nyquist criterion, the extensive computational complexity associated with the existing framework limits its clinical practicality. in this work, we propose an alternative recovery framework based on homotopic approximation of the l0-norm and extend the reconstruction problemto a multiscale formulation. in addition to several interesting theoretical properties, practical implementation of this technique effectively resorts to a simple iterative alternation between bilteral filtering and projection of themeasured k-space sample set that can be computed in a matter of seconds on a standard pc.
a slicing-based coherence measure for clusters of dti integral curves. we present a slicing-based coherence measure for clusters of dti integral curves. for a given cluster, we probe samples from the cluster by slicing it with a plane at regularly spaced locations parametrized by curve arc lengths. then we compute a stability measure based on the spatial relations between the projections of the curve points in individual slices and their change across the slices. we demonstrate its use in refining agglomerative hierarchical clustering results of dti curves that correspond to neural pathways. expert evaluation shows that refinement based on our measure can lead to improvement of clustering that is not possible directly by using standard methods.
automatic determination of arterial input function for dynamic contrast enhanced mri in tumor assessment. dynamic contrast enhanced mri (dce-mri) is today one of the most popular methods for tumor assessment. several pharmacokinetic models have been proposed to analyze dce-mri. most of them depend on an accurate arterial input function (aif). we propose an automatic and versatile method to determine the aif. the method has two stages, detection and segmentation, incorporating knowledge about artery structure, fluid kinetics, and the dynamic temporal property of dce-mri. we have applied our method in dce-mris of four different body parts: breast, brain, liver and prostate. the results show that we achieve average 89.5% success rate for 40 cases. the pharmacokinetic parameters computed from the automatic aif are highly agreeable with those from a manually derived aif (r2=0.89, p(t<=t)=0.19) and a semiautomatic aif (r2=0.98, p(t<=t)=0.01).
geodesic-loxodromes for diffusion tensor interpolation and difference measurement. in algorithms for processing diffusion tensor images, two common ingredients are interpolating tensors, and measuring the distance between them. we propose a new class of interpolation paths for tensors, termed geodesic-loxodromes, which explicitly preserve clinically important tensor attributes, such as mean diffusivity or fractional anisotropy, while using basic differential geometry to interpolate tensor orientation. this contrasts with previous riemannian and log-euclidean methods that preserve the determinant. path integrals of tangents of geodesic-loxodromes generate novel measures of over-all difference between two tensors, and of difference in shape and in orientation.
quantification of measurement error in dti: theoretical predictions and validation. the presence of rician noise in magnetic resonance imaging (mri) introduces systematic errors in diffusion tensor imaging (dti) measurements. this paper evaluates gradient direction schemes and tensor estimation routines to determine how to achieve the maximum accuracy and precision of tensor derived measures for a fixed amount of scan time. we present monte carlo simulations that quantify the effect of noise on diffusion measurements and validate these simulation results against appropriate in-vivo images. the predicted values of the systematic and random error caused by imaging noise are essential both for interpreting the results of statistical analysis and for selecting optimal imaging protocols given scan time limitations.
in-utero three dimension high resolution fetal brain diffusion tensor imaging. we present a methodology to achieve 3d high resolution in-utero fetal brain dti that shows excellent adc as well as promising fa maps. after continuous dti scanning to acquire a repeated series of parallel slices with 15 diffusion directions, image registration is used to realign the images to correct for fetal motion. once aligned, the diffusion images are treated as irregularly sampled data where each voxel is associated with an appropriately rotated diffusion direction, and used to estimate the diffusion tensor on a regular grid. the method has been tested successful on eight fetuses and has been validated on adults imaged at 1.5t.
prostate cancer probability maps based on ultrasound rf time series and svm classifiers. we describe a very efficient method based on ultrasound rf time series analysis and support vector machine classification for generating probabilistic prostate cancer colormaps to augment the biopsy process. to form the rf time series, we continuously record ultrasound rf echoes backscattered from tissue while the imaging probe and the tissue are stationary in position. in an in-vitro study involving 30 prostate specimens, we show that the features extracted from rf time series are significantly more accurate and sensitive compared to two other established categories of ultrasound-based tissue typing methods. the method results in an area under roc curve of 0.95 in 10-fold cross-validation.
finsler tractography for white matter connectivity analysis of the cingulum bundle. in this paper, we present a novel approach for the segmentation of white matter tracts based on finsler active contours. this technique provides an optimal measure of connectivity, explicitly segments the connecting fiber bundle, and is equipped with a metric which is able to utilize the directional information of high angular resolution data. we demonstrate the effectiveness of the algorithm for segmenting the cingulum bundle.
segmentation of myocardial volumes from real-time 3d echocardiography using an incompressibility constraint. real-time three-dimensional (rt3d) echocardiography is a new imaging modality that presents the unique opportunity to visualize the complex three-dimensional (3-d) shape and the motion of left ventricle (lv) in vivo. to take advantage of this opportunity, automatic segmentation of lv myocardium is essential. while there are a variety of efforts on the segmentation of lv endocardial (endo) boundaries, the segmentation of epicardial (epi) boundaries is still problematic. in this paper, we present a new approach of coupled-surfaces propagation to address this problem. our method is motivated by the idea that the volume of the myocardium is close to being constant during a cardiac cycle and takes this tight coupling as an important constraint. we employ two surfaces, each driven by the image-derived information that takes into account the ultrasound physics by modeling speckle using shifted rayleigh distribution while maintaining the coupling. by evolving two surfaces simultaneously, the final representation of myocardium is thus achieved. results from 328 sets of rt3d echocardiographic data are evaluated against the outlines of three observers. we show that the results from automatic segmentation are comparable to those from manual segmentation.
localized shape variations for classifying wall motion in echocardiograms. to quantitatively predict coronary artery diseases, automated analysis may be preferred to current visual assessment of left ventricular (lv) wall motion. in this paper, a novel automated classification method is presented which uses shape models with localized variations. these sparse shape models were built from four-chamber and two-chamber echocardiographic sequences using principal component analysis and orthomax rotations. the resulting shape parameters were then used to classify local wall-motion abnormalities of lv segments. various orthomax criteria were investigated. in all cases, higher classification correctness was achieved using significantly less shape parameters than before rotation. since pathologies are typically spatially localized, many medical applications involving local classification should benefit from orthomax parameterizations.
image guidance of intracardiac ultrasound with fusion of pre-operative images. this paper presents a method for registering 3d intracardiac echo (ice) to pre-operative images. a magnetic tracking sensor is integrated on the ice catheter tip to provide the 3d location and orientation. the user guides the catheter into the patient heart to acquire a series of ultrasound images covering the anatomy of the heart chambers. an automatic intensity-based registration algorithm is applied to align these ultrasound images with pre-operative images. one of the important applications is to help electrophysiology doctors to treat complicated atrial fibrillation cases. after registration, the doctor can see the position and orientation of the ice catheter and other tracked catheters inside the heart anatomy in real time. the image guidance provided by this technique may increase the ablation accuracy and reduce the amount of time for the electrophysiology procedures. we show successful image registration results from animal experiments.
3d reconstruction of internal organ surfaces for minimal invasive surgery. while minimally invasive surgery (mis) offers great benefits to patients compared with open surgery surgeons suffer from a restricted field-of-view and obstruction from instruments. we present a novel method for 3d reconstruction of soft tissue, which can provide a wider field-of-view with 3d information for surgeons, including restoration of missing data. the paper focuses on the use of structure from motion (sfm) techniques to solve the missing data problem and application of competitive evolutionary agents to improve the robustness to missing data and outliers. the method has been evaluated with synthetic data, images from a phantom heart model, and in vivo mis image sequences using the da vinci telerobotic surgical system.
automated segmentation of the liver from 3d ct images using probabilistic atlas and multi-level statistical shape model. an atlas-based automated liver segmentation method from 3d ct images is described. the method utilizes two types of atlases, that is, the probabilistic atlas (pa) and statistical shape model (ssm). voxel-based segmentation with pa is firstly performed to obtain a liver region, and then the obtained region is used as the initial region for subsequent ssm fitting to 3d ct images. to improve reconstruction accuracy especially for largely deformed livers, we utilize a multi-level ssm (ml-ssm). in ml-ssm, the whole shape is divided into patches, and principal component analysis is applied to each patches. to avoid the inconsistency among patches, we introduce a new constraint called the adhesiveness constraint for overlap regions among patches. in experiments, we demonstrate that segmentation accuracy improved by using the initial region obtained with pa and the introduced constraint for ml-ssm.
statistical and topological atlas based brain image segmentation. this paper presents a new atlas-based segmentation framework for the delineation of major regions in magnetic resonance brain images employing an atlas of the global topological structure as well as a statistical atlas of the regions of interest. a segmentation technique using fast marching methods and tissue classification is proposed that guarantees strict topological equivalence between the segmented image and the atlas. experimental validation on simulated and real brain images shows that the method is accurate and robust.
a boosted segmentation method for surgical workflow analysis. as demands on hospital efficiency increase, there is a stronger need for automatic analysis, recovery, and modification of surgical workflows. even though most of the previous work has dealt with higher level and hospital-wide workflow including issues like document management, workflow is also an important issue within the surgery room. its study has a high potential, e.g., for building context-sensitive operating rooms, evaluating and training surgical staff, optimizing surgeries and generating automatic reports. in this paper we propose an approach to segment the surgical workflow into phases based on temporal synchronization of multidimensional state vectors. our method is evaluated on the example of laparoscopic cholecystectomy with state vectors representing tool usage during the surgeries. the discriminative power of each instrument in regard to each phase is estimated using adaboost. a boosted version of the dynamic time warping (dtw) algorithm is used to create a surgical reference model and to segment a newly observed surgery. full cross-validation on ten surgeries is performed and the method is compared to standard dtw and to hidden markov models.
detection of spatial activation patterns as unsupervised segmentation of fmri data. in functional connectivity analysis, networks of interest are defined based on correlation with the mean time course of a user-selected 'seed' region. in this work we propose to simultaneously estimate the optimal representative time courses that summarize the fmri data well and the partition of the volume into a set of disjoint regions that are best explained by these representative time courses. our approach offers two advantages. first, is removes the sensitivity of the analysis to the details of the seed selection. second, it substantially simplifies group analysis by eliminating the need for a subject-specific threshold at which correlation values are deemed significant. this unsupervised technique generalizes connectivity analysis to situations where candidate seeds are difficult to identify reliably or are unknown. our experimental results indicate that the functional segmentation provides a robust, anatomically meaningful and consistent model for functional connectivity in fmri.
robotic assistance for ultrasound guided prostate brachytherapy. we present a robotically assisted prostate brachytherapy system and test results in training phantoms. the system consists of a transrectal ultrasound (trus) and a spatially co-registered robot integrated with an fda-approved commercial treatment planning system. the salient feature of the system is a small parallel robot affixed to the mounting posts of the template. the robot replaces the template interchangeably and uses the same coordinate system. established clinical hardware, workflow and calibration are left intact. in these experiments, we recorded the first insertion attempt without adjustment. all clinically relevant locations were reached. non-parallel needle trajectories were achieved. the pre-insertion transverse and rotational errors (measured with polaris optical tracker relative to the template's coordinate frame) were 0.25mm (std=0.17mm) and 0.75° (std=0.37°). the needle tip placement errors measured in trus were 1.04mm (std=0.50mm). the system is in phase-i clinical feasibility and safety trials, under institutional review board approval.