waashk/dblp huggingface dataset:
endoscopic navigation for minimally invasive suturing. manipulating small objects such as needles, screws or plates inside the human body during minimally invasive surgery can be very difficult for less experienced surgeons, due to the loss of 3d depth perception. this paper presents an approach for tracking a suturing needle using a standard endoscope. the resulting pose information of the needle is then used to generate artificial 3d cues on the 2d screen to optimally support surgeons during tissue suturing. additionally, if an external tracking device is provided to report the endoscope's position, the suturing needle can be tracked in a hybrid fashion with submillimeter accuracy. finally, a visual navigation aid can be incorporated, if a 3d surface is intraoperatively reconstructed from video or registered from preoperative imaging.
improving temporal fidelity in blast mri reconstruction. studies of myocardial motion using magnetic resonance imaging usually require multiple breath holds and several methods have been proposed in order to reduce the scan time. rapid imaging using k-t blast has gained much attention with its high reduction factors and image quality. temporal smoothing, however, may reduce the accuracy when assessing cardiac function. in the present work, a modified reconstruction filter is proposed, that preserves more of the high temporal frequencies. artificial decimation of a fully sampled data set was used to evaluate the reconstruction filter. compared to the conventional k-t blast reconstruction, the modified filter produced images with sharper temporal delineation of the myocardial walls. quantitative analysis by means of regional velocity estimation showed that the modified reconstruction filter produced more accurate velocity estimations.
deformable 2d-3d registration of the pelvis with a limited field of view, using shape statistics. our paper summarizes experiments for measuring the accuracy of deformable 2d-3d registration between sets of simulated x-ray images (drr's) and a statistical shape model of the pelvis bones, which includes x-ray attenuation information ("density"). in many surgical scenarios, the images contain a truncated view of the pelvis anatomy. our work specifically addresses this problem by examining different selections of truncated views as target images. our atlas is derived by applying principal component analysis to a population of up to 110 instance shapes. the experiments measure the registration error with a large and truncated fov. a typical accuracy of about 2 mm is achieved in the 2d- 3d registration, compared with about 1.4 mm of an "optimal" 3d-3d registration.
landmark correspondence optimization for coupled surfaces. volumetric layers are often encountered in medical images. unlike solid structures, volumetric layers are characterized by double and nested bounding surfaces. it is expected that better statistical models can be built by utilizing the surface coupleness rather than simply applying the landmarking method on each of them separately. we propose an approach to optimizing the landmark correspondence on the coupled surfaces by minimizing the description length that incorporates local thickness gradient. the evaluations are performed on a set of 2-d synthetic close coupled contours and a set of real-world open surfaces, the skull vaults. compared with performing landmarking separately on the coupled surfaces, the proposed method constructs models that have better generalization ability and specificity.
design and preliminary accuracy studies of an mri-guided transrectal prostate intervention system. this paper reports a novel system for magnetic resonance imaging (mri) guided transrectal prostate interventions, such as needle biopsy, fiducial marker placement, and therapy delivery. the system utilizes a hybrid tracking method, comprised of passive fiducial tracking for initial registration and subsequent incremental motion measurement along the degrees of freedom using fiber-optical encoders and mechanical scales. targeting accuracy of the system is evaluated in prostate phantom experiments. achieved targeting accuracy and procedure times were found to compare favorably with existing systems using passive and active tracking methods. moreover, the portable design of the system using only standard mri image sequences and minimal custom scanner interfacing allows the system to be easily used on different mri scanners.
a mr compatible mechatronic system to facilitate magic angle experiments . when imaging tendons and cartilage in a mri scanner, an increase in signal intensity is observed when they are oriented at 55 degrees with respect to bo (the "magic angle"). there is a clear clinical importance for considering this effect as part of the diagnosis of orthopaedic and other injury. experimental studies of this phenomenon have been made harder by practical difficulties of tissue positioning and orientation in the confined environment of cylindrical scanners. an mri compatible mechatronic system has been developed to position a variety of limbs inside the field of view of the scanner, to be used as a diagnostic and research tool. it is actuated with a novel pneumatic motor comprised of a heavily geared down air turbine, and is controlled in a closed loop using standard optical encoders. mr compatibility is demonstrated as well as the results of preliminary trials used to image the achilles tendon of human volunteers at different orientations. a 4 to 13 fold increase in signal at the tendon is observed at the magic angle.
automatic whole heart segmentation in static magnetic resonance image volumes. we present a fully automatic segmentation algorithm for the whole heart (four chambers, left ventricular myocardium and trunks of the aorta, the pulmonary artery and the pulmonary veins) in cardiac mr image volumes with nearly isotropic voxel resolution, based on shape-constrained deformable models. after automatic model initialization and reorientation to the cardiac axes, we apply a multi-stage adaptation scheme with progressively increasing degrees of freedom. particular attention is paid to the calibration of the mr image intensities. detailed evaluation results for the various anatomical heart regions are presented on a database of 42 patients. on calibrated images, we obtain an average segmentation error of 0.76mm.
tract-based morphometry. multisubject statistical analyses of diffusion tensor images in regions of specific white matter tracts have commonly measured only the mean value of a scalar invariant such as the fractional anisotropy (fa), ignoring the spatial variation of fa along the length of fiber tracts. we propose to instead perform tract-based morphometry (tbm), or the statistical analysis of diffusion mri data in an anatomical tract-based coordinate system. we present a method for automatic generation of white matter tract arc length parameterizations, based on learning a fiber bundle model from tractography from multiple subjects. our tract-based coordinate system enables tbm for the detection of white matter differences in groups of subjects. we present example tbm results from a study of interhemispheric differences in fa.
fiducial-free registration procedure for navigated bronchoscopy. navigated bronchoscopy has been developed by various groups within the last decades. systems based on ct data and electromagnetic tracking enable the visualization of the position and orientation of the bronchoscope, forceps, and biopsy tools within ct data. therefore registration between the tracking space and the ct volume is required. standard procedures are based on point-based registration methods that require selecting corresponding natural landmarks in both coordinate systems by the examiner. we developed a novel algorithm for a fully automatic registration procedure in navigated bronchoscopy based on the trajectory recorded during routine examination of the airways at the beginning of an intervention. the proposed system provides advantages in terms of an unchanged medical workflow and high accuracy. we compared the novel method with point-based and icp-based registration. experiments demonstrate that the novel method transforms up to 97% of tracking points inside the segmented airways, which was the best performance compared to the other methods.
analysis of deformation of the human ear and canal caused by mandibular movement. many hearing aid users experience physical discomfort when wearing their device. the main contributor to this problem is believed to be deformation of the ear and ear canal caused by movement of the mandible. physical discomfort results from added pressure on soft tissue areas in the ear. identifying features that can predict potential deformation is therefore important for identifying problematic cases in advance. a study on the physical deformation of the human ear and canal due to movement of the mandible is presented. the study is based on laser scannings of 30 pairs of ear impressions from 9 female and 21 male subjects. two impressions have been taken from each subject, one with open mouth, and one with the mouth closed. all impressions are registered using non-rigid surface registration and a shape model is built. from each pair of impressions a deformation field is generated and propagated to the shape model, enabling the building of a deformation model in the reference frame of the shape model. a relationship between the two models is established, showing that the shape variation can explain approximately 50% of the variation in the deformation model. an hypothesis test for significance of the deformations for each deformation field reveals that all subjects have significant deformation at tragus and in the canal. furthermore, a relation between the magnitude of the deformation and the gender of the subject is demonstrated. the results are successfully validated by comparing the outcome to the anatomy by using a single set of high resolution histological sectionings of the region of interest.
cell population tracking and lineage construction with spatiotemporal context. automated visual-tracking of cell populations in vitro using phase contrast time-lapse microscopy is vital for quantitative, systematic and high-throughput measurements of cell behaviors. these measurements include the spatiotemporal quantification of migration, mitosis, apoptosis, and cell lineage. this paper presents an automated cell tracking system that can simultaneously track and analyze thousands of cells. the system performs tracking by cycling through frame-by-frame track compilation and spatiotemporal track linking, combining the power of two tracking paradigms. we applied the system to a range of cell populations including adult stem cells. the system achieved tracking accuracies in the range of 83.8%-92.5%, outperforming previous work by up to 8%.
revisiting the evaluation of segmentation results: introducing confidence maps. we introduce a novel framework, called confidence maps estimating true segmentations (comets), to store segmentation references for medical images, combine multiple references, and measure the discrepancy between a segmented object and a reference. the core feature is the use of efficiently encoded confidence maps, which reflect the local variations of blur and the presence of nearby objects. local confidence values are defined from expert user input, and used to define a new discrepancy error measure, aimed to be directly interpreted quantitatively and qualitatively. we illustrate the use of this framework to compare different segmentation methods and tune a method's parameters.
non-local means variants for denoising of diffusion-weighted and diffusion tensor mri. diffusion tensor imaging (dt-mri) is very sensitive to corrupting noise due to the non linear relationship between the diffusion-weighted image intensities (dw-mri) and the resulting diffusion tensor. denoising is a crucial step to increase the quality of the estimated tensor field. this enhanced quality allows for a better quantification and a better image interpretation. the methods proposed in this paper are based on the non-local (nl) means algorithm. this approach uses the natural redundancy of information in images to remove the noise. we introduce three variations of the nl-means algorithms adapted to dw-mri and to dt-mri. experiments were carried out on a set of 12 diffusion-weighted images (dw-mri) of the same subject. the results show that the intensity based nl-means approaches give better results in the context of dt-mri than other classical denoising methods, such as gaussian smoothing, anisotropic diffusion and total variation.
simulation and fully automatic multimodal registration of medical ultrasound. the fusion of 3d freehand ultrasound with ct and cta has benefits for a variety of clinical applications, however a lot of manual work is usually required for correct registration. we developed new methods that allow one to simulate medical ultrasound from ct in real-time, reproducing the majority of ultrasonic imaging effects. the second novelty is a robust similarity measure that assesses the correlation of a combination of multiple signals extracted from ct with ultrasound, without knowing the influence of each signal. this serves as the foundation of a fully automatic registration, which aligns a freehand ultrasound sweep with the corresponding 3d modality using a rigid or an affine transformation model, without any manual interaction. we also present the used initialization, global and local parameter optimization schemes, and validation on abdominal cta and ultrasound imaging of 10 patients.
sources of variability in meg. this paper investigates and characterizes sources of variability in meg signals in multi-site, multi-subject studies. understanding these sources will help to develop efficient strategies for comparing and pooling data across repetitions of an experiment, across subjects, and across sites. in this work, we investigated somatosensory meg data collected at three different sites and applied variance component analysis and nonparametric kl divergence analysis in order to characterize the sources of variability. our analysis showed that inter-subject differences are the biggest factor in the signal variability. we demonstrated that the timing of the deflections is very consistent in the early somatosensory response, which justifies a direct comparison of deflection peak times acquired from different visits, subjects, and systems. compared with deflection peak times, deflection magnitudes have larger variation across sites; modeling of this variability is necessary for data pooling.
prostate implant reconstruction with discrete tomography. we developed a discrete tomography method for prostate implant reconstructions using only a limited number of x-ray projection images. a 3d voxel volume is reconstructed by back-projection and using distance maps generated from the projection images. the true seed locations are extracted from the voxel volume while false positive seeds are eliminated using a novel optimal geometry coverage model. the attractive feature of our method is that it does not require exact seed segmentation of the x-ray images and it yields near 100% correct reconstruction from only six images with an average reconstruction accuracy of 0.86 mm (std=0.46mm).
automatic segmentation of articular cartilage in magnetic resonance images of the knee. to perform cartilage quantitative analysis requires the accurate segmentation of each individual cartilage. in this paper we present a model based scheme that can automatically and accurately segment each individual cartilage in healthy knees from a clinical mr sequence (fat suppressed spoiled gradient recall). this scheme consists of three stages; the automatic segmentation of the bones, the extraction of the bone-cartilage interfaces (bci) and segmentation of the cartilages. the bone segmentation is performed using three-dimensional active shape models. the bci is extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. a cartilage thickness model then provides constraints and regularizes the cartilage segmentation performed from the bci. the accuracy and robustness of the approach was experimentally validated, with (patellar, tibial and femoral) cartilage segmentations having a median dsc of (0.870, 0.855, 0.870), performing significantly better than non-rigid registration (0.787, 0.814, 0.795). the total cartilage segmentation had an average dsc of (0.891), close to the (0.896) obtained using a semi-automatic watershed algorithm. the error in quantitative volume and thickness measures was (8.29, 4.94, 5.56)% and (0.19, 0.33, 0.10) mm respectively.
quantifying effect-specific mammographic density. a methodology is introduced for the automated assessment of structural changes of breast tissue in mammograms. it employs a generic machine learning framework and provides objective breast density measures quantifying the specific biological effects of interest. in several illustrative experiments on data from a clinical trial, it is shown that the proposed method can quantify effects caused by hormone replacement therapy (hrt) at least as good as standard methods. most interestingly, the separation of subpopulations using our approach is considerably better than the best alternative, which is interactive. moreover, the automated method is capable of detecting age effects where standard methodologies completely fail.
vessel and intracranial aneurysm segmentation using multi-range filters and local variances. segmentation of vessels and brain aneurysms on non-invasive and flow-sensitive phase contrast magnetic resonance angiographic (pcmra) images is essential in the detection of vascular diseases, in particular, intracranial aneurysms. in this paper, we devise a novel method based on multi-range filters and local variances to perform segmentation of vessels and intracranial aneurysms on pcmra images. the proposed method is validated and compared using a synthetic and numerical image volume and four clinical cases. it is experimentally shown that the proposed method is capable of segmenting vessels and aneurysms with various sizes on pcmra images.
pca-based magnetic field modeling : application for on-line mr temperature monitoring. magnetic resonance (mr) temperature mapping can be used to monitor temperature changes during minimally invasive thermal therapies. however, mr-thermometry contains artefacts caused by phase errors induced by organ motion in inhomogeneous magnetic fields. this paper proposes a novel correction strategy based on a principal component analysis (pca) to estimate magnetic field perturbation assuming a linear magnetic field variation with organ displacement. the correction method described in this paper consists of two steps: a magnetic field perturbation model is computed in a learning step; subsequently, during the intervention, this model is used to reconstruct the magnetic field perturbation corresponding to the actual organ position which in turns allow computation of motion corrected thermal maps.
classifier selection strategies for label fusion using large atlas databases. structural segmentations of brain mri can be generated by propagating manually labelled atlas images from a repository to a query subject and combining them. this method has been shown to be robust, consistent and increasingly accurate with increasing numbers of classifiers. it outperforms standard atlas-based segmentation but suffers, however, from problems of scale when the number of atlases is large. for a large repository and a particular query subject, using a selection strategy to identify good classifiers is one way to address problems of scale. this work presents and compares different classifier selection strategies which are applied to a group of 275 subjects with manually labelled brain mr images. we approximate an upper limit for the accuracy or overlap that can be achieved for a particular structure in a given subject and compare this with the accuracy obtained using classifier selection. the accuracy of different classifier selection strategies are also rated against the distribution of overlaps generated by random groups of classifiers.
tissue characterization using fractal dimension of high frequency ultrasound rf time series. this paper is the first report on the analysis of ultrasound rf echo time series acquired using high frequency ultrasound. we show that variations in the intensity of one sample of rf echo over time is correlated with tissue microstructure. to form the rf time series, a high frequency probe and a tissue sample were fixed in position and rf signals backscattered from the tissue were continuously recorded. the fractal dimension of rf time series was used as a feature for tissue classification. feature values acquired from different areas of one tissue type were statistically similar. for animal tissues with different cellular microstructure, we successfully used the fractal dimension of rf time series to distinguish segments as small as 20 microns with accuracies as high as 98%. the results of this study demonstrate that the analysis of rf time series is a promising approach for distinguishing tissue types with different cellular microstructure.
orthopedics surgery trainer with ppu-accelerated blood and tissue simulation. this paper presents a novel orthopedics surgery training system with both the components for modeling as well as simulating the deformation and visualization in an efficient way. by employing techniques such as optimization, segmentation and center line extraction, the modeling of deformable model can be completed with minimal manual involvement. the novel trainer can simulate rigid body, soft tissue and blood with state-of-the-art techniques, so that convincing deformation and realistic bleeding can be achieved. more important, newly released physics processing unit (ppu) is adopted to tackle the high requirement for physics related computations. experiment shows that the acceleration gain from ppu is significant for maintaining interactive frame rate under a complex surgical environments of orthopedics surgery.
improved statistical tre model when using a reference frame. target registration error (tre) refers to the uncertainty in localizing a point of interest after a point-based registration is performed. common in medical image registration, the metric is typically represented as a root-mean-square statistic. in the late 1990s, a statistical model was developed based on the rigid body definition of the fiducial markers and the localization error associated in measuring the fiducials. the statistical model assumed that the fiducial localizer error was isotropic, but recently the model was reworked to handle anisotropic fiducial localizer error (fle). in image guided surgery, the statistical model is used to predict the surgical tool tip tracking accuracy associated with optical spatial measurement systems for which anisotropic fle models are required. however, optical tracking systems often track the surgical tools relative to a patient based reference tool. here the formulation for modeling the tre of a surgical probe relative to a reference frame is developed mathematically and evaluated using a monte carlo simulation. the effectiveness of the statistical model is directly related to the fle model, the fiducial marker design and the distance from centroid to target.
automated model-based rib cage segmentation and labeling in ct images. we present a new model-based approach for an automated labeling and segmentation of the rib cage in chest ct scans. a mean rib cage model including a complete vertebral column is created out of 29 data sets. we developed a ray search based procedure for rib cage detection and initial model pose. after positioning the model, it was adapted to 18 unseen ct data. in 16 out of 18 data sets, detection, labeling, and segmentation succeeded with a mean segmentation error of less than 1.3 mm between true and detected object surface. in one case the rib cage detection failed, in another case the automated labeling.