waashk/dblp huggingface dataset:
proof of concept of a simple computer-assisted technique for correcting bone deformities. we propose a computer-assisted technique for correcting bone deformities using the ilizarov method. our technique is an improvement over prior art in that it does not require a tracking system, navigation hardware and software, or intraoperative registration. instead, we rely on a postoperative ct scan to obtain all of the information necessary to plan the correction and compute a correction schedule for the patient. our laboratory experiments using plastic phantoms produced deformity corrections accurate to within 3.0° of rotation and 1 mm of lengthening.
interactive contacts resolution using smooth surface representation. accurately describing interactions between medical devices and anatomical structures, or between anatomical structures themselves, is an essential step towards the adoption of computer-based medical simulation as an alternative to traditional training methods. however, while substantial work has been done in the area of real-time soft tissue modeling, little has been done to study the problem of contacts occurring during tissue manipulation. in this paper we introduce a new method for correctly handling complex contacts between various combination of rigid and deformable objects. our approach verifies signorini's law by combining lagrange multipliers and the status method to solve unilateral constraints. our method handles both concave and convex surfaces by using a displacement subdivision strategy, and the proposed algorithm allows interactive computation times even in very constrained situations. we demonstrate the efficiency of our approach in the context of interventional radiology, with the navigation of catheters and guidewires in tortuous vessels and with the deployment of coils to treat aneurysms.
autism diagnostics by 3d texture analysis of cerebral white matter gyrifications. the importance of accurate early diagnostics of autism that severely affects personal behavior and communication skills cannot be overstated. neuropathological studies have revealed an abnormal anatomy of the cerebral white matter (cwm) in autistic brains. we explore a possibility of distinguishing between autistic and normal brains by a quantitative shape analysis of cwm gyrifications on 3d proton density mri (pd-mri) images. our approach consists of (i) segmentation of the cwm on a 3d brain image using a deformable 3d boundary; (ii) extraction of gyrifications from the segmented cwm, and (iii) shape analysis to quantify thickness of the extracted gyrifications and classify autistic and normal subjects. the boundary evolution is controlled by two probabilistic models of visual appearance of 3d cwm: the learned prior and the current appearance model. initial experimental results suggest that the proposed 3d texture analysis is a promising supplement to the current techniques for diagnosing autism.
thoracoscopic surgical navigation system for cancer localization in collapsed lung based on estimation of lung deformation. we have developed a thoracoscopic surgical navigation system for lung cancer localization. in our system, the thoracic cage and mediastinum are localized using rigid registration between the intraoperatively digitized surface points and the preoperative ct surface model, and then the lung deformation field is estimated using nonrigid registration between the registered and digitized point datasets on the collapsed lung surface and the preoperative ct lung surface model to predict cancer locations. in this paper, improved methods on key components of the system are investigated to realize clinically acceptable usability and accuracy. firstly, we implement a non-contact surface digitizer under thoracoscopic control using an optically tracked laser pointer. secondly, we establish a rigid registration protocol which minimizes the influence of the deformation in different patient's positions by analyzing mr images of volunteers. these techniques were evaluated by in vitro and clinical experiments.
physically motivated enhancement of color images for fiber endoscopy. fiber optics are widely used in flexible endoscopes which are indispensable for many applications in diagnosis and therapy. computer-aided use of fiberscopes requires a digital sensor mounted at the proximal end. most commercially available cameras for endoscopy provide the images by means of a regular grid of color filters what is known as the bayer pattern. hence, the images suffer from false colored spatial moiré, which is further stressed by the downgrading fiber optic transmission yielding a honey comb pattern. to solve this problem we propose a new approach that extends the interpolation between known intensities of registered fibers to multi channel color applications. the inventive idea takes into account both the gaussian intensity distribution of each fiber and the physical color distribution of the bayer pattern. individual color factors for interpolation of each fiber area make it possible to simultaneously remove both the comb structure from the fiber bundle as well as the bayer pattern mosaicking from the sensor while preserving depicted structures and textures in the scene.
dynamic mri scan plane control for passive tracking of instruments and devices. this paper describes a novel image-based method for tracking robotic mechanisms and interventional devices during magnetic resonance image (mri)-guided procedures. it takes advantage of the multi-planar imaging capabilities of mri to optimally image a set of localizing fiducials for passive motion tracking in the image coordinate frame. the imaging system is servoed to adaptively position the scan plane based on automatic detection and localization of fiducial artifacts directly from the acquired image stream. this closed-loop control system has been implemented using an open-source software framework and currently operates with ge mri scanners. accuracy and performance were evaluated in experiments, the results of which are presented here.
a coupled finite element model of tumor growth and vascularization. we present a model of solid tumor growth which can account for several stages of tumorigenesis, from the early avascular phase to the angiogenesis driven proliferation. the model combines several previously identified components in a consistent framework, including neoplastic tissue growth, blood and oxygen transport, and angiogenic sprouting. first experiments with the framework and comparisons with observations made on solid tumors in vivo illustrate the plausibility of the approach. explanations of several experimental observations are naturally provided by the model. to the best of our knowledge this is the first report of a model coupling tumor growth and angiogenesis.
non-rigid registration of pre-procedural mr images with intra-procedural unenhanced ct images for improved targeting of tumors during liver radiofrequency ablations. in the united states, unenhanced ct is currently the most common imaging modality used to guide percutaneous biopsy and tumor ablation. the majority of liver tumors such as hepatocellular carcinomas are visible on contrast-enhanced ct or mri obtained prior to the procedure. yet, these tumors may not be visible or may have poor margin conspicuity on unenhanced ct images acquired during the procedure. non-rigid registration has been used to align images accurately, even in the presence of organ motion. however, to date, it has not been used clinically for radiofrequency ablation (rfa), since it requires significant computational infrastructure and often these methods are not sufficient robust. we have already introduced a novel finite element based method (fem) that is demonstrated to achieve good accuracy and robustness for the problem of brain shift in neurosurgery. in this current study, we adapt it to fuse pre-procedural mri with intra-procedural ct of liver. we also compare its performance with conventional rigid registration and two non-rigid registration methods: b-spline and demons on 13 retrospective datasets from patients that underwent rfa at our institution. fem non-rigid registration technique was significantly better than rigid (p<10-5), non-rigid b-spline (p<10- 4) and demons (p<10-4) registration techniques. the results of our study indicate that this novel technology may be used to optimize placement of rf applicator during ct-guided ablations.
new motion correction models for automatic identification of renal transplant rejection. acute rejection is the most common reason of graft failure after kidney transplantation and early detection is crucial to survive the transplanted kidney function. in this paper, we introduce a new approach for the automatic classification of normal and acute rejection transplants from dynamic contrast enhanced magnetic resonance imaging (dce-mri). the proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures. in the second step, new motion correction models are employed to account for both the global and local motion of the kidney due to patient moving and breathing. finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the kidney and used in the classification of normal and acute rejection transplants. in this paper, we will focus on the second and third steps and the first step is shown in detail in [1].
fast and robust analysis of dynamic contrast enhanced mri datasets. a fully automated method for quantitative analysis of dynamic contrast-enhanced mri data acquired with low and high field scanners, using spin echo and gradient echo sequences, depicting various joints is presented. the method incorporates efficient pre-processing techniques and a robust algorithm for quantitative assessment of dynamic signal intensity vs. time curves. it provides differentiated information to the reader regarding areas with the most active perfusion and permits depiction of different disease activity in separate compartments of a joint. additionally, it provides information on the speed of contrast agent uptake by various tissues. the method delivers objective and easily reproducible results, which have been favourably viewed by a number of medical experts.
prediction of respiratory motion with wavelet-based multiscale autoregression. in robotic radiosurgery, a photon beam source, moved by a robot arm, is used to ablate tumors. the accuracy of the treatment can be improved by predicting respiratory motion to compensate for system delay. we consider a wavelet-based multiscale autoregressive prediction method. the algorithm is extended by introducing a new exponential averaging parameter and the use of the moore-penrose pseudo inverse to cope with long-term signal dependencies and system matrix irregularity, respectively. in test cases, this new algorithm outperforms normalized lms predictors by as much as 50%. with real patient data, we achieve an improvement of around 5 to 10%.
regional homogeneity and anatomical parcellation for fmri image classification: application to schizophrenia and normal controls. this paper presents a discriminative model of multivariate pattern classification, based on functional magnetic resonance imaging (fmri) and anatomical template. as a measure of brain function, regional homogeneity (reho) is calculated voxel by voxel, and then a widely used anatomical template is applied on reho map to parcelate it into 116 brain regions. the mean and standard deviation of reho values in each region are extracted as features. pseudo-fisher linear discriminant analysis (pflda) is performed for training samples to generate discriminative model. classification experiments have been carried out in 48 schizophrenia patients and 35 normal controls. under a full leave-one-out (loo) cross-validation, correct prediction rate of 80% is achieved. anatomical parcellation process is proved useful to improve classification rate by a control experiment. the discriminative model shows its ability to reveal abnormal brain functional activities and identify people with schizophrenia.
mcmc curve sampling for image segmentation. we present an algorithm to generate samples from probability distributions on the space of curves. we view a traditional curve evolution energy functional as a negative log probability distribution and sample from it using a markov chain monte carlo (mcmc) algorithm. we define a proposal distribution by generating smooth perturbations to the normal of the curve and show how to compute the transition probabilities to ensure that the samples come from the posterior distribution. we demonstrate some advantages of sampling methods such as robustness to local minima, better characterization of multi-modal distributions, access to some measures of estimation error, and ability to easily incorporate constraints on the curve.
-space based non-photorealistic rendering for augmented reality. the increasing use of robotic assisted minimally invasive surgery (mis) provides an ideal environment for using augmented reality (ar) for performing image guided surgery. seamless synthesis of ar depends on a number of factors relating to the way in which virtual objects appear and visually interact with a real environment. traditional overlaid ar approaches generally suffer from a loss of depth perception. this paper presents a new ar method for robotic assisted mis, which uses a novel pq-space based non-photorealistic rendering technique for providing see-through vision of the embedded virtual object whilst maintaining salient anatomical details of the exposed anatomical surface. experimental results with both phantom and in vivo lung lobectomy data demonstrate the visual realism achieved for the proposed method and its accuracy in providing high fidelity ar depth perception.
bronchoscope tracking without fiducial markers using ultra-tiny electromagnetic tracking system and its evaluation in different environments. this paper presents a method for bronchoscope tracking without any fiducial markers using an ultra-tiny electromagnetic tracker (uemt) for a bronchoscopy guidance system. the proposed method calculates the transformation matrix, which shows the relationship between the coordinates systems of the pre-operative ct images and the uemt, by registering bronchial branches segmented from ct images and points measured by the uemt attached at the tip of a bronchoscope. we dynamically compute the transformation matrix for every pre-defined number of measurements. we applied the proposed method to a bronchial phantom in several experimental environments. the experimental results showed the proposed method can track a bronchoscope camera with about 3.3mm of target registration error (tre) for wood table environment and 4.0mm of tre for examination table environment.
segmentation and classification of breast tumor using dynamic contrast-enhanced mr images. accuracy of automatic cancer diagnosis is largely determined by two factors, namely, the precision of tumor segmentation, and the suitability of extracted features for discrimination between malignancy and benignancy. in this paper, we propose a new framework for accurate characterization of tumors in contrast enhanced mr images. first, a new graph cut based segmentation algorithm is developed for refining coarse manual segmentation, which allows precise identification of tumor regions. second, by considering serial contrast-enhanced images as a single spatio-temporal image, a spatio-temporal model of segmented tumor is constructed to extract spatio-temporal enhancement patterns (steps). steps are designed to capture not only dynamic enhancement and architectural features, but also spatial variations of pixel-wise temporal enhancement of the tumor. while temporal enhancement features are extracted through fourier transform, the resulting step framework captures spatial patterns of temporal enhancement features via moment invariants and rotation invariant gabor textures. high accuracy of the proposed framework is a direct consequence of this two pronged approach, which is validated through experiments yielding, for instance, an area of 0.97 under the roc curve.
fully automated and adaptive detection of amyloid plaques in stained brain sections of alzheimer transgenic mice. automated detection of amyloid plaques (ap) in post mortem brain sections of patients with alzheimer disease (ad) or in mouse models of the disease is a major issue to improve quantitative, standardized and accurate assessment of neuropathological lesions as well as of their modulation by treatment. we propose a new segmentation method to automatically detect amyloid plaques in congo red stained sections based on adaptive thresholds and a dedicated amyloid plaque/tissue modelling. a set of histological sections focusing on anatomical structures was used to validate the method in comparison to expert segmentation. original information concerning global amyloid load have been derived from 6 mouse brains which opens new perspectives for the extensive analysis of such a data in 3-d and the possibility to integrate in vivo-post mortem information for diagnosis purposes.
online estimation of the target registration error for -ocular optical tracking systems. for current surgical navigation systems optical tracking is state of the art. the accuracy of these tracking systems is currently determined statically for the case of full visibility of all tracking targets. we propose a dynamic determination of the accuracy based on the visibility and geometry of the tracking setup. this real time estimation of accuracy has a multitude of applications. for multiple camera systems it allows reducing line of sight problems and guaranteeing a certain accuracy. the visualization of these accuracies allows surgeons to perform the procedures taking to the tracking accuracy into account. it also allows engineers to design tracking setups interactively guaranteeing a certain accuracy. our model is an extension to the state of the art models of fitzpatrick et al. [1] and hoff et al. [2]. we model the error in the camera sensor plane. the error is propagated using the internal camera parameter, camera poses, tracking target poses, target geometry and marker visibility, in order to estimate the final accuracy of the tracked instrument.
a family of principal component analyses for dealing with outliers. principal component analysis (pca) has been widely used for dimensionality reduction in shape and appearance modeling. there have been several attempts of making pca robust against outliers. however, there are cases in which a small subset of samples may appear as outliers and still correspond to plausible data. the example of shapes corresponding to fractures when building a vertebra shape model is addressed in this study. in this case, the modeling of "outliers" is important, and it might be desirable not only not to disregard them, but even to enhance their importance. a variation on pca that deals naturally with the importance of outliers is presented in this paper. the technique is utilized for building a shape model of a vertebra, aiming at segmenting the spine out of lateral x-ray images. the results show that the algorithm can implement both an outlier-enhancing and a robust pca. the former improves the segmentation performance in fractured vertebrae, while the latter does so in the unfractured ones.
a comprehensive system for intraoperative 3d brain deformation recovery. during neurosurgery, brain deformation renders preoperative images unreliable for localizing pathologic structures. in order to visualize the current brain anatomy, it is necessary to nonrigidly warp these preoperative images to reflect the intraoperative brain. this can be accomplished using a biomechanical model driven by sparse intraoperative information. in this paper, a linear elastic model of the brain is developed which can infer volumetric brain deformation given the cortical surface displacement. this model was tested on both a realistic brain phantom and in vivo, proving its ability to account for large brain deformations. also, an efficient semiautomatic strategy for preoperative cortical feature detection is outlined, since accurate segmentation of cortical features can aid intraoperative cortical surface tracking.
global registration of multiple point sets: feasibility and applications in multi-fragment fracture fixation. an algorithm to globally register multiple 3d data sets (point sets) within a general reference frame is proposed. the algorithm uses the unscented kalman filter algorithm to simultaneously compute the registration transformations that map the data sets together, and to calculate the variances of the registration parameters. the data sets are either randomly generated, or collected from a set of fractured bone phantoms using computed tomography (ct) images. the algorithm robustly converges for isotropic gaussian noise that could have perturbed the point coordinates in the data sets. it is also computationally efficient, and enables real-time global registration of multiple data sets, with applications in computer-assisted orthopaedic trauma surgery.
intra-operative 3d guidance in prostate brachytherapy using a non-isocentric c-arm. intra-operative guidance in transrectal ultrasound (trus) guided prostate brachytherapy requires localization of inserted radioactive seeds relative to the prostate. seeds were reconstructed using a typical c-arm, and exported to a commercial brachytherapy system for dosimetry analysis. technical obstacles for 3d reconstruction on a non-isocentric c-arm included pose-dependent c-arm calibration; distortion correction; pose estimation of c-arm images; seed reconstruction; and c-arm to trus registration. in precision-machined hard phantoms with 40-100 seeds, we correctly reconstructed 99.8% seeds with a mean 3d accuracy of 0.68 mm. in soft tissue phantoms with 45-87 seeds and clinically realistic 15° c-arm motion, we correctly reconstructed 100% seeds with an accuracy of 1.3 mm. the reconstructed 3d seed positions were then registered to the prostate segmented from trus. in a phase-1 clinical trial, so far on 4 patients with 66-84 seeds, we achieved intraoperative monitoring of seed distribution and dosimetry. we optimized the 100% prescribed iso-dose contour by inserting an average of 3.75 additional seeds, making intra-operative dosimetry possible on a typical c-arm, at negligible additional cost to the existing clinical installation.
a clinically motivated 2-fold framework for quantifying and classifying immunohistochemically stained specimens. motivated by the current limitations of automated quantitative image analysis in discriminating among intracellular immunohistochemical (ihc) staining patterns, this paper presents a two-fold approach for ihc characterization that utilizes both the protein stain information and the surrounding tissue architecture. through the use of a color unmixing algorithm, stained tissue sections are automatically decomposed into the ihc stain, which visualizes the target protein, and the counterstain which provides an objective indication of the underlying histologic architecture. feature measures are subsequently extracted from both staining planes. in order to characterize the ihc expression pattern, this approach exploits the use of a non-traditional feature based on textons. novel biologically motivated filter banks are introduced in order to derive texture signatures for different ihc staining patterns. systematic experiments using this approach were used to classify breast cancer tissue microarrays which had been previously prepared using immuno-targeted nuclear, cytoplasmic, and membrane stains.