waashk/dblp huggingface dataset:
inverse c-arm positioning for interventional procedures using real-time body part detection. the automation and speedup of interventional therapy and diagnostic workflows is a crucial issue. one way to improve these workflows is to accelerate the image acquisition procedures by fully automating the patient setup. this paper describes a system that performs this task without the use of markers or other prior assumptions. it returns metric coordinates of the 3-d body shape in real-time for inverse positioning. this is achieved by the application of an emerging technology, called time-of-flight (tof) sensor. a tof sensor is a cost-efficient, off-the-shelf camera which provides more than 40,000 3-d points in real-time. the first contribution of this paper is the incorporation of this novel imaging technology (tof) in interventional imaging. the second contribution is the ability of a c-arm system to position itself with respect to the patient prior to the acquisition. we are using the 3-d surface information of the patient to partition the body into anatomical sections. this is achieved by a fast two-stage classification process. the system computes the iso-center for each detected region. to verify our system we performed several tests on the iso-center of the head. firstly, the reproducibility of the head iso-center computation was evaluated. we achieved an accuracy of (x: 1.73±1.11 mm/y: 1.87±1.31 mm/z: 2.91±2.62 mm). secondly, a c-arm head scan of a body phantom was setup. our system automatically aligned the iso-center of the head with the c-arm iso-center. here we achieved an accuracy of ± 1 cm, which is within the accuracy of the patient table control.
non-rigid reconstruction of the beating heart surface for minimally invasive cardiac surgery. this paper presents a new method to reconstruct the beating heart surface based on the non-rigid structure from motion technique using preprocessed endoscopic images. first the images captured at the same phase within each heart cycle are automatically extracted from the original image sequence to reduce the dimension of the deformation subspace. then the remaining residual non-rigid motion is restricted to lie within a low-dimensional subspace and a probabilistic model is used to recover the 3d structure and camera motion simultaneously. outliers are removed iteratively based on the reprojection error. missing data are also recovered with an expectation maximization algorithm. as a result the camera can move around the operation scene to build a 3d surface with a wide field-of-view for intra-operative procedures. the method has been evaluated with synthetic data, heart phantom data, and in vivo data from a da vinci surgical system.
optimal matching for prostate brachytherapy seed localization with dimension reduction. in prostate brachytherapy, x-ray fluoroscopy has been used for intra-operative dosimetry to provide qualitative assessment of implant quality. more recent developments have made possible 3d localization of the implanted radioactive seeds. this is usually modeled as an assignment problem and solved by resolving the correspondence of seeds. it is, however, np-hard, and the problem is even harder in practice due to the significant number of hidden seeds. in this paper, we propose an algorithm that can find an optimal solution from multiple projection images with hidden seeds. it solves an equivalent problem with reduced dimensional complexity, thus allowing us to find an optimal solution in polynomial time. simulation results show the robustness of the algorithm. it was validated on 5 phantom and 18 patient datasets, successfully localizing the seeds with detection rate of ¿ 97.6 % and reconstruction error of ≤ 1.2 mm. this is considered to be clinically excellent performance.
robotic force stabilization for beating heart intracardiac surgery. the manipulation of fast moving, delicate tissues in beating heart procedures presents a considerable challenge to surgeons. we present a new robotic force stabilization system that assists surgeons by maintaining a constant contact force with the beating heart. the system incorporates a novel, miniature uniaxial force sensor that is mounted to surgical instrumentation to measure contact forces during surgical manipulation. using this sensor in conjunction with real-time tissue motion information derived from 3d ultrasound, we show that a force controller with feed-forward motion terms can provide safe and accurate force stabilization in an in vivo contact task against the beating mitral valve annulus. this confers a 50% reduction in force fluctuations when compared to a standard force controller and a 75% reduction in fluctuations when compared to manual attempts to maintain the same force.
evaluation of lobar biomechanics during respiration using image registration. the human lungs are divided into five independent compartments called lobes. the lobar fissures separate the lung lobes. it is hypothesized that the lobar surfaces slide against each other during respiration. we propose a method to evaluate the sliding motion of the lobar surfaces during respiration using lobe-by-lobe mass-preserving non-rigid image registration. we measure lobar sliding by evaluating the relative displacement on both sides of the fissure. the results show a superior-inferior gradient in the magnitude of lobar sliding. we compare whole-lung-based registration accuracy to lobe-by-lobe registration accuracy using vessel bifurcation landmarks.
optical biopsy mapping for minimally invasive cancer screening. the quest for providing tissue characterization and functional mapping during minimally invasive surgery (mis) has motivated the development of new surgical tools that extend the current functional capabilities of mis. miniaturized optical probes can be inserted into the instrument channel of standard endoscopes to reveal tissue cellular and subcellular microstructures, allowing excision-free optical biopsy. one of the limitations of such a point based imaging and tissue characterization technique is the difficulty of tracking probed sites in vivo. this prohibits large area surveillance and integrated functional mapping. the purpose of this paper is to present an image-based tracking framework by combining a semi model-based instrument tracking method with vision-based simultaneous localization and mapping. this allows the mapping of all spatio-temporally tracked biopsy sites, which can then be re-projected back onto the endoscopic video to provide a live augmented view in vivo, thus facilitating re-targeting and serial examination of potential lesions. the proposed method has been validated on phantom data with known ground truth and the accuracy derived demonstrates the strength and clinical value of the technique. the method facilitates a move from the current point based optical biopsy towards large area multi-scale image integration in a routine clinical environment.
probabilistic region matching in narrow-band endoscopy for targeted optical biopsy. recent advances in biophotonics have enabled in-vivo, in-situ histopathology for routine clinical applications. the non-invasive nature of these optical `biopsy' techniques, however, entails the difficulty of identifying previously visited biopsy locations, particularly for surveillance examinations. this paper presents a novel region-matching approach for narrow-band endoscopy to facilitate retargeting the optical biopsy sites. the task of matching sparse affine covariant image regions is modelled in a markov random field (mrf) framework. the proposed model incorporates appearance based region similarities as well as spatial correlations of neighbouring regions. in particular, a geometric constraint that is robust to deviations in relative positioning of the detected regions is introduced. in the proposed model, the appearance and geometric constraints are evaluated in the same space (photometry), allowing for their seamless integration into the mrf objective function. the performance of the method as compared to the existing state-of-the-art is evaluated with both in-vivo and simulation datasets with varying levels of visual complexities.
adjusting the neuroimaging statistical inferences for nonstationarity. in neuroimaging cluster-based inference has generally been found to be more powerful than voxel-wise inference [1]. however standard cluster-based methods assume stationarity (constant smoothness), while under nonstationarity clusters are larger in smooth regions just by chance, making false positive risk spatially variant. hayasaka et al. [2] proposed a random field theory (rft) based nonstationarity adjustment for cluster inference and validated the method in terms of controlling the overall family-wise false positive rate. the rft-based methods, however, have never been directly assessed in terms of homogeneity of local false positive risk. in this work we propose a new cluster size adjustment that accounts for local smoothness, based on local empirical cluster size distributions and a two-pass permutation method. we also propose a new approach to measure homogeneity of local false positive risk, and use this method to compare the rft-based and our new empirical adjustment methods. we apply these techniques to both cluster-based and a related inference, threshold-free cluster enhancement (tfce). using simulated and real data we confirm the expected heterogeneity in false positive risk with unadjusted cluster inference but find that rft-based adjustment does not fully eliminate heterogeneity; we also observe that our proposed empirical adjustment dramatically increases the homogeneity and tfce inference is generally quite robust to nonstationarity.
non-rigid image registration with uniform gradient spherical patterns. in this paper, we propose a new feature based non-rigid image registration method for dealing with two important issues. first, in order to establish reliable anatomical correspondence between template and subject images, efficient and distinctive region descriptor is needed as intensity information alone maybe insufficient. second, since interference factors such as monotonic gray-level bias fields are commonly existed during the imaging process, the registration algorithm should be robust against such factors. there are two main contributions presented in this paper. (1) a new region descriptor, named uniform gradient spherical pattern (ugsp), is proposed to extract the geometric features from input images. ugsp encodes second order voxel interaction information. (2) the ugsp feature is rotation and monotonic gray-level bias field invariant. the proposed method is integrated with the markov random field (mrf) labeling framework to formulate the registration process. the ¿-expansion algorithm is used to optimize the corresponding mrf energy function. the proposed method is evaluated on both the simulated and real 3d databases obtained from brainweb and ibsr respectively and compared with other state-of-the-art registration methods. experimental results show that the proposed method gives the highest registration accuracy among all the compared methods on both databases.
automatic segmentation of the pulmonary lobes from fissures, airways, and lung borders: evaluation of robustness against missing data. automatic segmentation of structures with missing or invisible borders is a challenging task. since structures in the lungs are related, humans use contextual and shape information to infer the position of invisible borders. an example of a task in which the borders are often incomplete or invisible is the segmentation of the pulmonary lobes. in this paper, a fully automatic segmentation of the pulmonary lobes in chest ct scans is presented. the method is especially designed to be robust to incomplete fissures by incorporating contextual information from automatic lung, fissure, and bronchial tree segmentations, as well as shape information. since the method relies on the result of automatic segmentations, it is important that the method is robust against failure of one or more of these segmentation methods. in an extensive experiment on 10 chest ct scans with manual segmentations, the robustness of the method to incomplete fissures and missing input segmentations is shown. in a second experiment on 100 chest ct scans with incomplete fissures, the method is shown to perform well.
quantifying brain connectivity: a comparative tractography study. in this paper, we compare a representative selection of current state-of-the-art algorithms in diffusion-weighted magnetic resonance imaging (dwmri) tractography, and propose a novel way to quantitatively define the connectivity between brain regions. as criterion for the comparison, we quantify the connectivity computed with the different methods. we provide initial results using diffusion tensor, spherical deconvolution, ball-and-stick model, and persistent angular structure (pas) along with deterministic and probabilistic tractography algorithms on a human dwi dataset. the connectivity is presented for a representative selection of regions in the brain in matrices and connectograms.our results show that fiber crossing models are able to reveal connections between more brain areas than the simple tensor model. probabilistic approaches show in average more connected regions but lower connectivity values than deterministic methods.
a coaxial laser endoscope with arbitrary spots in endoscopic view for fetal surgery. in this paper, we describe a rigid endoscope that transmits a laser beam coaxially to arbitrary points in the endoscopic view, mainly for treatment of twin-to-twin transfusion syndrome. the endoscope consists of a hotmirror for coaxial transmission of visible light and a nd:yag laser beam, and galvanometers for controlling the beam irradiation angle. we evaluated the transmission efficiency of the laser power, the spot size through the endoscope and accuracy in positioning the beam. the maximum laser transmission efficiency was 39% and the spot diameter was 2.2---3.2 mm at a distance of 10---20 mm. the positioning accuracy was mostly within 1.0 mm in the endoscopic view at the distance. the average laser power density on the spot was estimated to be 170---370 w/cm2, and a chicken liver was successfully coagulated by changing the laser beam irradiation angle.
two-compartment models of the diffusion mr signal in brain white matter. this study aims to identify the minimum requirements for an accurate model of the diffusion mr signal in white matter of the brain. we construct a hierarchy of two-compartment models of white matter from combinations of simple models for the intra and extra-cellular spaces. we devise a new diffusion mri protocol that provides measurements with a wide range of parameters for diffusion sensitization both parallel and perpendicular to white matter fibres. we use the protocol to acquire data from a fixed rat brain, which allows us to fit, study and compare the different models. the results show that models which incorporate pore size describe the measurements most accurately. the best fit comes from combining a full diffusion tensor (dt) model of the extra-cellular space with a cylindrical intra-cellular component.
a novel method for registration of us/mr of the liver based on the analysis of us dynamics. radio frequency ablation of liver cancer is a minimally invasive alternative to open surgery. typically, the preoperative planning is done on an mr (or ct) scan, while the intervention relies on ultrasound (us) guidance. registration of intra-operative us and preoperative mr (or ct) would assist navigation and increase the confidence of rfa needle positioning. in this paper we present a novel method for registration of us and mr images of the liver. hepatic vessels are extracted from 2d us by an algorithm that models us dynamics. it generates 2d probability maps representing hepatic vessels which are then combined into probability volumes. a multi-resolution registration framework performs registration of the pre-processed mr with two 3d vessel probability images. the accuracy, robustness and speed of the method were assessed by registering eight us/mr datasets. high robustness (86%) and reasonable accuracy (1.98°, 4.10mm), acceptable for the rfa clinical application, suggest that the method has a good potential for intra-operative use.
disco: a coherent diffeomorphic framework for brain registration under exhaustive sulcal constraints. neuroimaging at the group level requires spatial normalization of individual structural data. we propose a geometric approach that consists in matching a series of cortical surfaces through diffeomorphic registration of their sulcal imprints. the resulting 3d transforms naturally extends to the entire mri volumes. the diffeomorphic sulcal-based cortical (disco) registration integrates two recent technical outcomes: 1) the automatic extraction, identification and simplification of numerous sulci from t1-weighted mri data series hereby revealing the sulcal imprint and 2) the measure-based diffeomorphic registration of those crucial anatomical landmarks. we show how the disco registration may be used to elaborate a sulcal template which optimizes the distribution of constraints over the entire cortical ribbon. disco was evaluated through a group of 20 individual brains. quantitative and qualitative indices attest how this approach may improve both alignment of sulcal folds and overlay of gray and white matter volumes at the group level.
using real-time fmri to control a dynamical system by brain activity classification. we present a method for controlling a dynamical system using real-time fmri. the objective for the subject in the mr scanner is to balance an inverted pendulum by activating the left or right hand or resting. the brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to the pendulum. the state of the inverted pendulum is shown to the subject in a pair of vr goggles. the subject was able to balance the inverted pendulum during several minutes, both with real activity and imagined activity. in each classification 9000 brain voxels were used and the response time for the system to detect a change of activity was on average 2-4 seconds. the developments here have a potential to aid people with communication disabilities, such as locked in people. another future potential application can be to serve as a tool for stroke and parkinson patients to be able to train the damaged brain area and get real-time feedback for more efficient training.
a computer model of soft tissue interaction with a surgical aspirator. surgical aspirators are one of the most frequently used neurosurgical tools. effective training on a neurosurgery simulator requires a visually and haptically realistic rendering of surgical aspiration. however, there is little published data on mechanical interaction between soft biological tissues and surgical aspirators. in this study an experimental setup for measuring tissue response is described and results on calf brain and a range of phantom materials are presented. local graphical and haptic models are proposed. they are simple enough for real-time application, and closely match the observed tissue response. tissue resection (cutting) with suction is simulated using a volume sculpting approach. a simulation of suction is presented as a demonstration of the effectiveness of the approach.
belief propagation based segmentation of white matter tracts in dti. this paper presents a belief propagation approach to the segmentation of the major white matter tracts in diffusion tensor images of the human brain. unlike tractography methods that sample multiple fibers to be bundled together, we define a markov field directly on the diffusion tensors to separate the main fiber tracts at the voxel level. a prior model of shape and direction guides a full segmentation of the brain into known fiber tracts; additional, unspecified fibers; and isotropic regions. the method is evaluated on various data sets from an atlasing project, healthy subjects, and multiple sclerosis patients.
a demons algorithm for image registration with locally adaptive regularization. thirion's demons [1] is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. it approximately solves the diffusion registration problem [2] by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by gaussian convolution. in this article, we show how the demons algorithm can be generalized to allow image-driven locally adaptive regularization [3,4] in a manner that preserves both the linear complexity and ease of implementation of the original demons algorithm. we show that the proposed algorithm exhibits lower target registration error and requires less computational effort than the original demons algorithm on the registration of serial chest ct scans of patients with lung nodules.
a riemannian framework for orientation distribution function computing. compared with diffusion tensor imaging (dti), high angular resolution imaging (hardi) can better explore the complex microstructure of white matter. orientation distribution function (odf) is used to describe the probability of the fiber direction. fisher information metric has been constructed for probability density family in information geometry theory and it has been successfully applied for tensor computing in dti. in this paper, we present a state of the art riemannian framework for odf computing based on information geometry and sparse representation of orthonormal bases. in this riemannian framework, the exponential map, logarithmic map and geodesic have closed forms. and the weighted frechet mean exists uniquely on this manifold. we also propose a novel scalar measurement, named geometric anisotropy (ga), which is the riemannian geodesic distance between the odf and the isotropic odf. the renyi entropy $h_{\frac{1}{2}}$ of the odf can be computed from the ga. moreover, we present an affine-euclidean framework and a log-euclidean framework so that we can work in an euclidean space. as an application, lagrange interpolation on odf field is proposed based on weighted frechet mean. we validate our methods on synthetic and real data experiments. compared with existing riemannian frameworks on odf, our framework is model-free. the estimation of the parameters, i.e. riemannian coordinates, is robust and linear. moreover it should be noted that our theoretical results can be used for any probability density function (pdf) under an orthonormal basis representation.
task versus subtask surgical skill evaluation of robotic minimally invasive surgery. evaluating surgical skill is a time consuming, subjective, and difficult process. this paper compares two methods of identifying the skill level of a subject given motion data from a benchtop surgical task. in the first method, we build discrete hidden markov models at the task level, and test against these models. in the second method, we build discrete hidden markov models of surgical gestures, called surgemes, and evaluate skill at this level. we apply these techniques to 57 data sets collected from the da vinci surgical system. our current techniques have achieved accuracy levels of 100% using task level models and known gesture segmentation, 95% with task level models and unknown gesture segmentation, and 100% with the surgeme level models in correctly identifying the skill level. we observe that, although less accurate, the second method requires less prior label information. also, the surgeme level classification provided more insights into what subjects did well, and what they did poorly.