waashk/dblp huggingface dataset:
a constrained non-rigid registration algorithm for use in prostate image-guided radiotherapy. a constrained non-rigid registration (cnrr) algorithm for use in updating prostate external beam image-guided radiotherapy treatment plans is presented in this paper. the developed algorithm is based on a multi-resolution cubic b-spline ffd transformation and has been tested and verified using 3d ct images from 10 sets of real patient data acquired from 4 different patients on different treatment days. the registration can be constrained to any combination of the prostate, rectum, bladder, pelvis, left femur, and right femur. the cnrr was tested with 5 different combinations of constraints and each test significantly outperformed both rigid and non-rigid registration at aligning constrained bones and critical organs. the cnrr was then used to update the treatment plans to account for articulated, rigid bone motion and non-rigid organ deformation. each updated treatment plan outperformed the original treatment plan by increasing radiation dosage to the prostate and lowering radiation dosage to the rectum and bladder.
detection of dti white matter abnormalities in multiple sclerosis patients. the emergence of new modalities such as diffusion tensor imaging (dti) is of great interest for the characterization and the temporal study of multiple sclerosis (ms). dti indeed gives information on water diffusion within tissues and could therefore reveal alterations in white matter fibers before being visible in conventional mri. however, recent studies generally rely on scalar measures derived from the tensors such as fa or md instead of using the full tensor itself. therefore, a certain amount of information is left unused.in this article, we present a framework to study the benefits of using the whole diffusion tensor information to detect statistically significant differences between each individual ms patient and a database of control subjects. this framework, based on the comparison of the ms patient dti and a mean dti atlas built from the control subjects, allows us to look for differences both in normally appearing white matter but also in and around the lesions of each patient. we present a study on a database of 11 ms patients, showing the ability of the dti to detect not only significant differences on the lesions but also in regions around them, enabling an early detection of an extension of the ms disease.
a global approach for automatic fibroscopic video mosaicing in minimally invasive diagnosis. recent developments in bio-photonics have called for the need of bringing cellular and molecular imaging modalities to an in vivo --- in situ setting to allow for real-time tissue characterization and functional assessment. before such techniques can be used effectively in routine clinical environments, it is necessary to address the visualization requirement for linking point based optical biopsy to large area tissue visualization. this paper presents a novel approach for fibered endoscopic video mosaicing that permits wide region tissue visualization. a feature-based registration method is used to register the frames of the endoscopic video sequence by taking into account the characteristics of fibroscopic imaging such as non-linear lens distortion and high-frequency fiber optic facet pattern. the registration is combined with an efficient optimization scheme in order to align all input frames in a globally consistent way. an evaluation on phantom and ex vivo tissue images allowing free-hand camera motion is presented.
findings in schizophrenia by tract-oriented dt-mri analysis. this paper presents a tract-oriented analysis of diffusion tensor (dt) images of the human brain. we demonstrate that unlike the commonly used roi-based methods for population studies, our technique is sensitive to the local variation of diffusivity parameters along the fiber tracts. we show the strength of the proposed approach in identifying the differences in schizophrenic data compared to controls. statistically significant drops in fractional anisotropy are observed along the genu and bilaterally in the splenium, as well as an increase in principal eigenvalue in uncinate fasciculus. this is the first tract-oriented clinical study in which an anatomical atlas is used to guide the algorithm.
dynamic thermal modeling of the normal and tumorous breast under elastic deformation. to quantify the complex relationships between (1) the temperature, and temperature differences, on the surface of the breast as recorded by infrared thermal imaging and (2) the underlying physiological and pathological factors, we have developed a dynamic finite element method for comprehensive modeling of both the thermal and elastic properties of normal and tumorous breast tissues. in the steady state, the gravity-induced deformation is found to cause markedly asymmetric surface temperatures even though all thermal-elastic properties are symmetrical. in the dynamic state, the time course of breast thermal imaging in cold-stress and thermal-recovery procedures is found to be useful in characterizing the origins of the thermal contrast on the breast surface. the tumor-induced thermal contrast has slower temporal behavior than the deformation-induced thermal contrast on the breast surface, which may lead to improvements in breast-tumor diagnosis.
toward unsupervised classification of calcified arterial lesions. there is growing evidence that calcified arterial deposits play a crucial role in the pathogenesis of cardiovascular disease. this paper investigates the challenging problem of unsupervised calcified lesion classification. we propose an algorithm, us-calc (unsupervised calcified arterial lesion classification), that discriminates arterial lesions from non-arterial lesions. the proposed method first mines the characteristics of calcified lesions using a novel optimization criterion and then identifies a subset of lesion features which is optimal for classification. second, a two stage clustering is deployed to discriminate between arterial and non-arterial lesions. a histogram intersection distance measure is incorporated to determine cluster proximity. the clustering hierarchies are carefully validated and the final clusters are determined by a new intra-cluster compactness measure. experimental results indicate an average accuracy of approximately 80% on a database of electron beam ct heart scans.
motion robust magnetic susceptibility and field inhomogeneity estimation using regularized image restoration techniques for fmri. in functional mri, head motion may cause dynamic nonlinear field-inhomogeneity changes, especially with large out-of-plane rotations. this may lead to dynamic geometric distortion or blurring in the time series, which may reduce activation detection accuracy. the use of image registration to estimate dynamic field inhomogeneity maps from a static field map is not sufficient in the presence of such rotations. this paper introduces a retrospective approach to estimate magnetic susceptibility induced field maps of an object in motion, given a static susceptibility induced field map and the associated object motion parameters. it estimates a susceptibility map from a static field map using regularized image restoration techniques, and applies rigid body motion to the former. the dynamic field map is then computed using susceptibility voxel convolution. the method addresses field map changes due to out-of-plane rotations during time series acquisition and does not involve real time field map acquisitions.
automatic tracking of escherichia coli bacteria. in this paper, we present an automatic method for estimating the trajectories of escherichia coli bacteria from in vivo phase-contrast microscopy videos. to address the low-contrast boundaries in cellular images, an adaptive kernel-based technique is applied to detect cells in sequence of frames. then a novel matching gain measure is introduced to cope with the challenges such as dramatic changes of cells' appearance and serious overlapping and occlusion. for multiple cell tracking, an optimal matching strategy is proposed to improve the handling of cell collision and broken trajectories. the results of successful tracking of escherichia coli from various phase-contrast sequences are reported and compared with manually-determined trajectories, as well as those obtained from existing tracking methods. the stability of the algorithm with different parameter values is also analyzed and discussed.
cortical surface thickness as a classifier: boosting for autism classification. we study the problem of classifying an autistic group from controls using structural image data alone, a task that requires a clinical interview with a psychologist. because of the highly convoluted brain surface topology, feature extraction poses the first obstacle. a clinically relevant measure called the cortical thickness has shown promise but yields a rather challenging learning problem --- where the dimensionality of the distribution is extremely large and the training set is small. by observing that each point on the brain cortical surface may be treated as a "hypothesis", we propose a new algorithm for lpboosting (with truncated neighborhoods) for this problem. in addition to learning a high quality classifier, our model incorporates topological priors into the classification framework directly --- that two neighboring points on the cortical surface (hypothesis pairs) must have similar discriminative qualities. as a result, we obtain not just a label { + 1, ¿ 1} for test items, but also an indication of the "discriminative regions" on the cortical surface. we discuss the formulation and present interesting experimental results.
interactive separation of segmented bones in ct volumes using graph cut. we present a fast, interactive method for separating bones that have been collectively segmented from a ct volume. given user-provided seed points, the method computes the separation as a multi-way cut on a weighted graph constructed from the binary, segmented volume. by properly designing and weighting the graph, we show that the resulting cut can accurately be placed at bone-interfaces using only a small number of seed points even when the data is noisy. the method has been implemented with an interactive graphical interface, and used to separate the 12 human foot bones in 10 ct volumes. the interactive tool produced compatible result with a ground-truth separation, generated by a completely manual labelling procedure, while reducing the human interaction time from a mean of 2.4 hours per volume in manual labelling down to approximately 18 minutes.
diffusion tensor image registration using tensor geometry and orientation features. this paper presents a method for deformable registration of diffusion tensor (dt) images that integrates geometry and orientation features into a hierarchical matching framework. the geometric feature is derived from the structural geometry of diffusion and characterizes the shape of the tensor in terms of prolateness, oblateness, and sphericity of the tensor. local spatial distributions of the prolate, oblate, and spherical geometry are used to create an attribute vector of geometric feature for matching. the orientation feature improves the matching of the wm fiber tracts by taking into account the statistical information of underlying fiber orientations. these features are incorporated into a hierarchical deformable registration framework to develop a diffusion tensor image registration algorithm. extensive experiments on simulated and real brain dt data establish the superiority of this algorithm for deformable matching of diffusion tensors, thereby aiding in atlas creation. the robustness of the method makes it potentially useful for group-based analysis of dt images acquired in large studies to identify disease-induced and developmental changes.
interactive simulation of embolization coils: modeling and experimental validation. coil embolization offers a new approach to treat aneurysms. this medical procedure is namely less invasive than an open-surgery as it relies on the deployment of very thin platinum-based wires within the aneurysm through the arteries. when performed intracranially, this procedure must be particularly accurate and therefore carefully planned and performed by experienced radiologists. a simulator of the coil deployment represents an interesting and helpful tool for the physician by providing information on the coil behavior. in this paper, an original modeling is proposed to obtain interactive and accurate simulations of coil deployment. the model takes into account geometric nonlinearities and uses a shape memory formulation to describe its complex geometry. an experimental validation is performed in a contact-free environment to identify the mechanical properties of the coil and to quantitatively compare the simulation with real data. computational performances are also measured to insure an interactive simulation.
regularized discriminative direction for shape difference analysis. the "discriminative direction" has been proven useful to reveal the subtle difference between two anatomical shape classes. when a shape moves along this direction, its deformation will best manifest the class difference detected by a kernel classifier. however, we observe that such a direction cannot maintain a shape's "anatomical" correctness, introducing spurious difference. to overcome this drawback, we develop a regularized discriminative direction by requiring a shape to conform to its population distribution when it deforms along the discriminative direction. instead of iterative optimization, an analytic solution is provided to directly work out this direction. experimental study shows its superior performance in detecting and localizing the difference of hippocampal shapes for sex. the result is supported by other independent research in the same domain.
group statistics of dti fiber bundles using spatial functions of tensor measures. we present a framework for hypothesis testing of differences between groups of dti fiber tracts. an anatomical, tract-oriented coordinate system provides a basis for estimating the distribution of diffusion properties. the parametrization of sampled, smooth functions is normalized across a population using dti atlas building. functional data analysis, an extension of multivariate statistics to continuous functions is applied to the problem of hypothesis testing and discrimination. b-spline models of fractional anisotropy (fa) and frobenius norm measures are analyzed jointly. plots of the discrimination direction provide a clinical interpretation of the group differences. the methodology is tested on a pediatric study of subjects aged one and two years.
a symmetry-based method for the determination of vertebral rotation in 3d. in the past, a number of methods were proposed for quantitative assessment of vertebral rotation from three-dimensional (3d) images. however, these methods were based on manual identification of distinctive anatomical landmarks, required manual determination of cross-sections from 3d images, and measured only axial vertebral rotation instead of the rotation in 3d. in this paper, we propose an automated method for quantitative assessment of vertebral rotation in 3d that is based on finding the planes of vertebral symmetry by matching image intensity gradients on both sides of each plane. the method was evaluated on 28 images of normal and pathological vertebrae, obtained by computed tomography (ct) and magnetic resonance (mr). for each vertebra, final angle displacements of 200 initial angle displacements, uniformly distributed within 30° from manually obtained reference angles, were obtained. the results show that by the proposed method, vertebral rotation can be successfully estimated in 3d with an average accuracy of 1.0° and precision of 0.5°.
adaptive boundary conditions for physically based follow-up breast mr image registration. this paper presents an algorithm for non-rigid registration of breast mri follow-up images that compensates for differences in patient positioning while maintaining real anatomical and pathological changes. the proposed method uses a biomechanical model to constrain the deformation of the internal breast tissue according to elastic continuum mechanics, which is driven by suitable boundary conditions that align the breast surfaces in the images to be registered. typically, such boundary conditions impose one-to-one surface point correspondences that are established a priori. we investigate alternative, more flexible boundary conditions that do not depend on fixed point correspondences and do not assume completely accurate breast surface segmentation in both images. more specifically, we allow for sliding motion of one surface over the other during deformation as well as for restricted motion perpendicular to the initially segmented boundary surface, based on the internal elastic forces and local intensity information. we evaluate the impact of different boundary conditions on registration quality from the subtraction images obtained for repeated scans of healthy volunteers with intermediate repositioning, using rigid body and free form whole volume intensity based registration for comparison, and also present initial results for actual patient data. our results demonstrate a drastic reduction in subtraction artifacts using our model, without compromising the biomechanical validity of the deformation field such as unrealistically large local volume changes as with traditional voxel intensity based registration.
brain mr image segmentation using local and global intensity fitting active contours/surfaces. in this paper, we present an improved region-based active contour/surface model for 2d/3d brain mr image segmentation. our model combines the advantages of both local and global intensity information, which enable the model to cope with intensity inhomogeneity. we define an energy functional with a local intensity fitting term and an auxiliary global intensity fitting term. in the associated curve evolution, the motion of the contour is driven by a local intensity fitting force and a global intensity fitting force, induced by the local and global terms in the proposed energy functional, respectively. the influence of these two forces on the curve evolution is complementary. when the contour is close to object boundaries, the local intensity fitting force became dominant, which attracts the contour toward object boundaries and finally stops the contour there. the global intensity fitting force is dominant when the contour is far away from object boundaries, and it allows more flexible initialization of contours by using global image information. the proposed model has been applied to both 2d and 3d brain mr image segmentation with promising results.
level set based surface capturing in 3d medical images. brain aneurysm rupture has been reported to be directly related to the size of aneurysms. the current method used to determine aneurysm size is to manually measure the width of the neck and height of the dome on a computer screen. because aneurysms usually have complicated shapes, using the size of the aneurysm neck and dome may not be accurate and may overlook important geometrical information. in this paper we present a level set based illusory surface algorithm to capture the aneurysms from the vascular tree. since the aneurysms are described by level set functions, not only the volume but also the curvature of aneurysms can be computed for medical studies. experiments and comparisons with models used for capturing illusory contours in 2d images are performed. this includes applications to clinical image data demonstrating the procedure of accurately capturing a middle cerebral artery aneurysm.
a new stochastic framework for accurate lung segmentation. new techniques for more accurate unsupervised segmentation of lung tissues from low dose computed tomography (ldct) are proposed. in this paper we describe ldct images and desired maps of regions (lung and the other chest tissues) by a joint markov-gibbs random field model (mgrf) of independent image signals and interdependent region labels but focus on most accurate model identification. to better specify region borders, each empirical distribution of signals is precisely approximated by a linear combination of discrete gaussians (lcdg) with positive and negative components. we modify a conventional expectation-maximization (em) algorithm to deal with the lcdg and develop a sequential em-based technique to get an initial lcdg-approximation for the modified em algorithm. the initial segmentation based on the lcdg-models is then iteratively refined using a mgrf model with analytically estimated potentials. experiments on real data sets confirm high accuracy of the proposed approach.
real-time 3d reconstruction for collision avoidance in interventional environments. with the increased presence of automated devices such as c-arms and medical robots and the introduction of a multitude of surgical tools, navigation systems and patient monitoring devices, collision avoidance has become an issue of practical value in interventional environments. in this paper, we present a real-time 3d reconstruction system for interventional environments which aims at predicting collisions by building a 3d representation of all the objects in the room. the 3d reconstruction is used to determine whether other objects are in the working volume of the device and to alert the medical staff before a collision occurs. in the case of c-arms, this allows faster rotational and angular movement which could for instance be used in 3d angiography to obtain a better reconstruction of contrasted vessels. the system also prevents staff to unknowingly enter the working volume of a device. this is of relevance in complex environments with many devices. the recovered 3d representation also opens the path to many new applications utilizing this data such as workflow analysis, 3d video generation or interventional room planning. to validate our claims, we performed several experiments with a real c-arm that show the validity of the approach. this system is currently being transferred to an interventional room in our university hospital.
combination of intraoperative 5-aminolevulinic acid-induced fluorescence and 3-d mr imaging for guidance of robotic laser ablation for precision neurosurgery. a combination of 5-aminolevulinic acid (5-ala)-induced fluorescence and three-dimensional (3-d) magnetic resonance imaging (mri) of a brain tumor has been incorporated into a robotic laser ablation neurosurgery system. 5-ala is a non-fluorescent prodrug that leads to intracellular accumulation of fluorescent protoporphyrins ix (ppix) in malignant glioma. the ppix tends to accumulate in pathological lesions, and emits red fluorescence when excited by blue light. this fluorescence is illuminated with laser excitation, enables intra-operative identification of the position of a tumor and provides guidance for resection with laser photocoagulation. the information provided by the mri is enhanced by the 5-ala fluorescence data, and this enhanced information is integrated into a robotic laser ablation system. the accuracy of the fluorescent measurement of the tumor is improved using high-precision spectral analysis. the fluorescence assists in the detection of malignant brain tumors intraoperatively and improves their removal rate.
colon unfolding via skeletal subspace deformation. we present an efficient method to digitally straighten a colon volume using mesh skinning, a technique well known in computer graphics to deform a polygonal mesh attached to a skeleton hierarchy. in our case, the colon centerline is used as the skeleton structure and the polyhedral model of the lumen as the skin that is to be deformed as the centerline is straightened. once the colon has been straightened, we use standard rendering techniques to compute the virtual dissection. our approach is significantly more efficient than previously proposed techniques.