waashk/dblp huggingface dataset:
single fiber optical coherence tomography microsurgical instruments for computer and robot-assisted retinal surgery. we present initial prototype and preliminary experimental demonstration of a new class of microsurgical instruments that incorporate common path optical coherence tomography (cp-oct) capabilities. these instruments may be used freehand or with robotic assistance. we describe a prototype 25 gauge microsurgical pick incorporating a single 125 (m diameter optical fiber interfaced to a fourier domain cp-oct system developed in our laboratory. for initial experimentation, we have interfaced this instrument with an extremely precise, cooperatively controlled robot. we describe the tool, system design, and demonstration of three control methods on simple phantom models: 1) enforce ment of safety constraints preventing unintentional collisions of the instrument with the retinal surface; 2) the ability to scan the probe across a surface while maintaining a constant distance offset; and 3) the ability to place the pick over a subsurface target identified in a scan and then penetrate the surface to hit the target.
joint segmentation of image ensembles via latent atlases. spatial priors, such as probabilistic atlases, play an important role in mri segmentation. however, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. we therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas. instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble. the proposed method is based on probabilistic principles but is solved using partial differential equations (pdes) and energy minimization criteria. we evaluate the method by segmenting 50 brain mr volumes. segmentation accuracy for cortical and subcortical structures approaches the quality of state-of-the-art atlas-based segmentation results, suggesting that the latent atlas method is a reasonable alternative when existing atlases are not compatible with the data to be processed.
endoscopic orientation correction. an open problem in endoscopic surgery (especially with flexible endoscopes) is the absence of a stable horizon in endoscopic images. with our "endorientation" approach image rotation correction, even in non-rigid endoscopic surgery (particularly notes), can be realized with a tiny mems tri-axial inertial sensor placed on the tip of an endoscope. it measures the impact of gravity on each of the three orthogonal accelerometer axes. after an initial calibration and filtering of these three values the rotation angle is estimated directly. achievable repetition rate is above the usual endoscopic video frame rate of 30hz; accuracy is about one degree. the image rotation is performed in real-time by digitally rotating the analog endoscopic video signal. improvements and benefits have been evaluated in animal studies: coordination of different instruments and estimation of tissue behavior regarding gravity related deformation and movement was rated to be much more intuitive with a stable horizon on endoscopic images.
asymmetric image-template registration. a natural requirement in pairwise image registration is that the resulting deformation is independent of the order of the images. this constraint is typically achieved via a symmetric cost function and has been shown to reduce the effects of local optima. consequently, symmetric registration has been successfully applied to pairwise image registration as well as the spatial alignment of individual images with a template. however, recent work has shown that the relationship between an image and a template is fundamentally asymmetric. in this paper, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. we instantiate our model within a log-domain diffeomorphic registration framework. our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates.
fast tensor image morphing for elastic registration. we propose a novel algorithm, called fast tensor image morphing for elastic registration or f-timer. f-timer leverages multiscale tensor regional distributions and local boundaries for hierarchically driving deformable matching of tensor image volumes. registration is achieved by aligning a set of automatically determined structural landmarks, via solving a soft correspondence problem. based on the estimated correspondences, thin-plate splines are employed to generate a smooth, topology preserving, and dense transformation, and to avoid arbitrary mapping of non-landmark voxels. to mitigate the problem of local minima, which is common in the estimation of high dimensional transformations, we employ a hierarchical strategy where a small subset of voxels with more distinctive attribute vectors are first deployed as landmarks to estimate a relatively robust low-degrees-of-freedom transformation. as the registration progresses, an increasing number of voxels are permitted to participate in refining the correspondence matching. a scheme as such allows less conservative progression of the correspondence matching towards the optimal solution, and hence results in a faster matching speed. results indicate that better accuracy can be achieved by f-timer, compared with other deformable registration algorithms [1, 2], with significantly reduced computation time cost of 4---14 folds.
estimating orientation distribution functions with probability density constraints and spatial regularity. high angular resolution diffusion imaging (hardi) has become an important magnetic resonance technique for in vivo imaging. current techniques for estimating the diffusion orientation distribution function (odf), i.e., the probability density function of water diffusion along any direction, do not enforce the estimated odf to be nonnegative or to sum up to one. very often this leads to an estimated odf which is not a proper probability density function. in addition, current methods do not enforce any spatial regularity of the data. in this paper, we propose an estimation method that naturally constrains the estimated odf to be a proper probability density function and regularizes this estimate using spatial information. by making use of the spherical harmonic representation, we pose the odf estimation problem as a convex optimization problem and propose a coordinate descent method that converges to the minimizer of the proposed cost function. we illustrate our approach with experiments on synthetic and real data.
bias of least squares approaches for diffusion tensor estimation from array coils in dt-mri. least squares (ls) and its weighted version are standard techniques to estimate the diffusion tensor (dt) from diffusion weighted images (dwi). they require to linearize the problem by computing the logarithm of the dwi. for the single-coil rician noise model it has been shown that this model does not introduce a significant bias, but for multiple array coils and parallel imaging, the noise cannot longer be modeled as rician. as a result the validity of ls approaches is not assured. an analytical study of noise statistics for a multiple coil system is carried out, together with the weighted ls formulation and noise analysis for this model. results show that the bias in the computation of the components of the dt may be comparable to their variance in many cases, stressing the importance of unbiased filtering previous to dt estimation.
closed-form jensen-renyi divergence for mixture of gaussians and applications to group-wise shape registration. in this paper, we propose a generalized group-wise non-rigid registration strategy for multiple unlabeled point-sets of unequal cardinality, with no bias toward any of the given point-sets. to quantify the divergence between the probability distributions --- specifically mixture of gaussians --- estimated from the given point sets, we use a recently developed information-theoretic measure called jensen-renyi (jr) divergence. we evaluate a closed-form jr divergence between multiple probabilistic representations for the general case where the mixture models differ in variance and the number of components. we derive the analytic gradient of the divergence measure with respect to the non-rigid registration parameters, and apply it to numerical optimization of the group-wise registration, leading to a computationally efficient and accurate algorithm. we validate our approach on synthetic data, and evaluate it on 3d cardiac shapes.
3-d respiratory motion compensation during ep procedures by image-based 3-d lasso catheter model generation and tracking. radio-frequency catheter ablation of the pulmonary veins attached to the left atrium is usually carried out under fluoroscopy guidance. two-dimensional x-ray navigation may involve overlay images derived from a static pre-operative 3-d volumetric data set to add anatomical details. however, respiratory motion may impair the utility of static overlay images for catheter navigation. we developed a system for image-based 3-d motion estimation and compensation as a solution to this problem for which no previous solution is yet known. it is based on 3-d catheter tracking involving 2-d/3-d registration. a biplane x-ray c-arm system is used to image a special circumferential (lasso) catheter from two directions. in the first step of the method, a 3-d model of the device is reconstructed. 3-d respiratory motion at the site of ablation is then estimated by tracking the reconstructed model in 3-d from biplane fluoroscopy. in our experiments, the circumferential catheter was tracked in 231 biplane fluoro frames (462 monoplane fluoro frames) with an average 2-d tracking error of 1.0 mm ± 0.5 mm.
cervical vertebrae tracking in video-fluoroscopy using the normalized gradient field. for patients with neck problems valuable functional and diagnostic information can be obtained from a fluoroscopy video of a flexion-extension movement of the cervical spine. in most cases physicians have to manually extract the vertebrae, making the analysis of these video sequences tedious and time consuming. in this paper we propose an automatic fast and precise method for tracking cervical vertebrae. our method relies only on a rough selection of template areas of each vertebra in a single frame of the video sequence. compared to existing automated methods, no contours need to be extracted and no vertebra segmentation is required. tracking is done with a normalized gradient field, using only the gradient orientations as features. experimental results show that the algorithm is robust and able to track the vertebrae accurately even if they are partially occluded or if a disc prosthesis is present.
improving pit-pattern classification of endoscopy images by a combination of experts. the diagnosis of colorectal cancer is usually supported by a staging system, such as the duke or tnm system. in this work we discuss computer---aided pit---pattern classification of surface structures observed during high---magnification colonoscopy in order to support dignity assessment of colonic polyps. this is considered a quite promising approach because it allows in vivo staging of colorectal lesions. since recent research work has shown that the characteristic surface structures of the colon mucosa exhibit texture characteristics, we employ a set of texture image features in the wavelet-domain and propose a novel classifier combination approach which is similar to a combination of experts. the experimental results of our work show superior classification performance compared to previous approaches on both a two-class (non-neoplastic vs. neoplastic) and a more complicated six-class (pit---pattern) classification problem.
wide-angle intraocular imaging and localization. vitreoretinal surgeries require accuracy and dexterity that is often beyond the capabilities of human surgeons. untethered robotic devices that can achieve the desired precision have been proposed, and localization information is required for their control. since the interior of the human eye is externally observable, vision can be used for their localization. in this paper we examine the effects of the human eye optics on imaging and localizing intraocular devices. we propose a method for wide-angle intraocular imaging and localization. we demonstrate accurate localization with experiments in a model eye.
in vivo oct coronary imaging augmented with stent reendothelialization score. the aim of this study is to automatically assess reendothelialization of stents at an accuracy of down to a few microns by analyzing endovascular optical coherence tomography (oct) sequences. vessel wall and struts are automatically detected and complete distance map is then computed from sparse distances measured between wall and struts by thin-plate spline (tps) interpolation. a reendothelialization score is mapped onto the geometry of the coronary artery segment. accuracy and robustness are increased by taking into account the inhomogeneity of datapoints and integrating in the same framework orthogonalized forward selection of support points, optimal selection of regularization parameters by generalized cross-validation (gcv) and rejection of detection outliers. the comparison against manual expert measurements for a phantom study and 12 in vivo stents demonstrates no significant discordance with variability of the order of the strut thickness.
non-rigid registration of high angular resolution diffusion images represented by gaussian mixture fields. in this paper, we present a novel algorithm for non-rigidly registering two high angular resolution diffusion weighted mris (hardi), each represented by a gaussian mixture field (gmf). we model the non-rigid warp by a thin-plate spline and formulate the registration problem as the minimization of the l2 distance between the two given gmfs. the key mathematical contributions of this work are, (i) a closed form expression for the derivatives of this objective function with respect to the parameters of the registration and (ii) a novel and simpler re-orientation scheme based on an extension to the "preservation of principle directions" technique. we present results of our algorithm's performance on several synthetic and real hardi data sets.
statistical regularization of deformation fields for atlas-based segmentation of bone scintigraphy images. the construction and application of statistical models of deformations based on non-rigid image registration methods have gained recent popularity. this paper presents the application of such a model to restricting a general-purpose registration algorithm to anatomically plausible solutions. specifically, the morphon registration method is used for atlas-based segmentation of bone scintigraphy images. from a training set of 734 images, a model of characteristic deformation fields is built and used for regularizing the registration of 113 test images. results show that around 300 training images and 30 principal modes are sufficient for building a useful model. the segmentation succeeded in 106 of 113 test images.
task-optimal registration cost functions. in this paper, we propose a framework for learning the parameters of registration cost functions --- such as the tradeoff between the regularization and image similiarity term --- with respect to a specific task. assuming the existence of labeled training data, we specialize the framework for the task of localizing hidden labels via image registration. we learn the parameters of the weighted sum of squared differences (wssd) image similarity term that are optimal for the localization of brodmann areas (bas) in a new subject based on cortical geometry. we demonstrate state-of-the-art localization of v1, v2, ba44 and ba45.
novel endoscope system with plasma flushing for off-pump cardiac surgery. the purpose of this study is to develop a new endoscope for performing simple surgical tasks inside a cardiac atrium/chamber filled with blood, i.e., for performing "off-pump" cardiac surgeries. in general, it is very difficult to observe the inner wall of the vessels containing circulating blood because the light from the endoscope is scattered by the red blood cells. "plasma flushing" performed using the separator system is developed to observe the inner side of the heart filled with blood and to remove blood cells from the front of the endoscope tip. the system was used in in vitro quantitative measurement of the device performance and in vivo experiments on a swine. in these experiments, we successfully obtained high-resolution images of the interior of the heart during off-pump surgery.
a non-rigid registration method for serial microct mouse hindlimb images. we present a new method for the non-rigid registration of serial mouse microct images which undergo potentially large changes in the positions of the legs due to articulation. while non-rigid registration methods have been extensively used in the evaluation of individual organs, application in whole body imaging has been limited, primarily because the scale of possible displacements and deformations is large resulting in poor convergence of most methods. our method is based on the extended demons algorithm that uses a level-set representation of the mouse skin and skeleton as an input, and composed of three steps reflecting the natural physical movements of bony structures. we applied our method to the registration of serial microct mouse images demonstrating encouraging performances as compared to competitive techniques.
a combined surface and volumetric registration (savor) framework to study cortical biomarkers and volumetric imaging data. constructing a one to one correspondence between whole brain mr image scans is a problem of critical importance in neuroimaging analyses. we present a framework to combine the strength of both surface-based and volumetric-based analyses for consistent, bijective data transfer between brain coordinate systems.
gyral folding pattern analysis via surface profiling. human cortical folding pattern has been studied for decades. this paper proposes a gyrus scale folding pattern analysis technique via cortical surface profiling. firstly, we sample the cortical surface into 2d profiles and model them using power function. this step provides both the flexibility of representing arbitrary shape by profiling and the compactness of representing shape by parametric modeling. secondly, based on the estimated model parameters, we extract affine-invariant features on the cortical surface and apply the affinity propagation clustering algorithm to parcellate the cortex into regions with different shape patterns. finally, a second-round surface profiling is performed on the parcellated cortical regions, and the number of hinges is detected to describe the gyral folding pattern. experiments demonstrate that our method could successfully classify human gyri into 2-hinge, 3-hinge and 4-hinge gyri. the proposed method has the potential to significantly contribute to automatic segmentation and recognition of cortical gyri.
robust medical images segmentation using learned shape and appearance models. we propose a novel parametric deformable model controlled by shape and visual appearance priors learned from a training subset of co-aligned medical images of goal objects. the shape prior is derived from a linear combination of vectors of distances between the training boundaries and their common centroid. the appearance prior considers gray levels within each training boundary as a sample of a markov-gibbs random field with pairwise interaction. spatially homogeneous interaction geometry and gibbs potentials are analytically estimated from the training data. to accurately separate a goal object from an arbitrary background, empirical marginal gray level distributions inside and outside of the boundary are modeled with adaptive linear combinations of discrete gaussians (lcdg). due to the analytical shape and appearance priors and a simple expectation-maximization procedure for getting the object and background lcdg, our segmentation is considerably faster than with most of the known geometric and parametric models. experiments with various goal images confirm the robustness, accuracy, and speed of our approach.
mr to ultrasound image registration for guiding prostate biopsy and interventions. a method is described for registering preoperative magnetic resonance (mr) to intraoperative transrectal ultrasound (trus) images of the prostate gland. a statistical motion model (smm) of the prostate is first built using training data provided by biomechanical simulations of the motion of a patient-specific finite element model, derived from a preoperative mr image. the smm is then registered to a 3d trus image by maximising the likelihood of the shape of an smm instance given a voxel-intensity-based feature, which represents an estimate of normal vector at the surface of the prostate gland. using data acquired from 7 patients, the accuracy of registering t2 mr to 3d trus images was evaluated using anatomical landmarks inside the gland. the results show that the proposed registration method has a root-mean-square target registration error of 2.66 mm.
image guidance for spinal facet injections using tracked ultrasound. anesthetic nerve blocks are a common therapy performed in hospitals around the world to alleviate acute and chronic pain. tracking systems have shown considerable promise in other forms of therapy, but little has been done to apply this technology in the field of anesthesia. we are developing a guidance system for combining tracked needles with non-invasive ultrasound (us) and patient-specific geometric models. in experiments with phantoms two augmented reality (ar) guidance systems were compared to the exclusive use of us for lumbar facet injection therapy. anesthetists and anesthesia residents were able to place needles within 0.57mm of the intended targets using our ar systems compared to 5.77 mm using us alone. a preliminary cadaver study demonstrated the system was able to accurately place radio opaque dye on targets. the combination of real time us with tracked tools and ar guidance has the potential to replace ct and fluoroscopic guidance, thus reducing radiation dose to patients and clinicians, as well as reducing health care costs.
tracked regularized ultrasound elastography for targeting breast radiotherapy. tracked ultrasound elastography can be used for guidance in partial breast radiotherapy by visualizing the hard scar tissue around the lumpectomy cavity. for clinical success, the elastography method needs to be robust to the sources of decorrelation between ultrasound images, specifically fluid motions inside the cavity, change of the appearance of speckles caused by compression or physiologic motions, and out-of-plane motion of the probe. in this paper, we present a novel elastography technique that is based on analytic minimization of a regularized cost function. the cost function incorporates similarity of rf data intensity and displacement continuity, making the method robust to small decorrelations present throughout the image. we also exploit techniques from robust statistics to make the method resistant to large decorrelations caused by sources such as fluid motion. the analytic displacement estimation works in real-time. moreover, the tracked data, used for targeting the radiotherapy, is exploited for discarding frames with excessive out-of-plane motion. simulation, phantom and patient results are presented.