waashk/dblp huggingface dataset:
surface-based texture and morphological analysis detects subtle cortical dysplasia. focal cortical dysplasia (fcd), a malformation of cortical development, is an important cause of pharmacoresistant epilepsy. small fcd lesions are difficult to distinguish from normal cortex and remain often overlooked on radiological mri inspection. this paper presents a method to detect small fcd lesions on t1-mri relying on surface-based features that model their textural and morphometric characteristics. the automatic detection was performed by a two step classification. first, a vertex-wise classifier based on a neural-network bagging trained on manual labels. then, a cluster-wise classification designed to remove false positive clusters. the method was tested on 19 patients with small fcd. at the first classification step, 18/19 (95%) lesions were detected. the second classification step kept 13/19 (68%) lesions and decreased efficiently the amount of false positive. this new approach may assist the presurgical evaluation of patients with intractable epilepsy, especially those with unremarkable mri findings.
subject-specific biomechanical simulation of brain indentation using a meshless method. we develop a meshless method for simulating soft organ deformation. the method is motivated by simple, automatic model creation for real-time simulation. our method is meshless in the sense that deformation is calculated at nodes that are not part of an element mesh. node placement is almost arbitrary. fully geometrically nonlinear total lagrangian formulation is used. geometric integration is performed over a regular background grid that does not conform to the simulation geometry. explicit time integration is used via the central difference method. to validate the method we simulate indentation of a swine brain and compare the results to experimental data.
towards an identification of tumor growth parameters from time series of images. in cancer treatment, understanding the aggressiveness of the tumor is essential in therapy planning and patient follow-up. in this article, we present a novel method for quantifying the speed of invasion of gliomas in white and grey matter from time series of magnetic resonance (mr) images. the proposed approach is based on mathematical tumor growth models using the reaction-diffusion formalism. the quantification process is formulated by an inverse problem and solved using anisotropic fast marching method yielding an efficient algorithm. it is tested on a few images to get a first proof of concept with promising new results.
registration of lung tissue between fluoroscope and ct images: determination of beam gating parameters in radiotherapy. significant research has been conducted in radiation beam gating technology to manage target and organ motions in radiotherapy treatment of cancer patients. as more and more on-board imagers are installed onto linear accelerators, fluoroscopic imaging becomes readily available at the radiation treatment stage. thus, beam gating parameters, such as beam-on timing and beam-on window can be potentially determined by employing image registration between treatment planning ct images and fluoroscopic images. we propose a new registration method on deformable soft tissue between fluoroscopic images and drr (digitally reconstructed radiograph) images from planning ct images using active shape models. we present very promising results of our method applied to 30 clinical datasets. these preliminary results show that the method is very robust for the registration of deformable soft tissue. the proposed method can be used to determine beam-on timing and treatment window for radiation beam gating technology, and can potentially greatly improve radiation treatment quality.
a hybrid system for the semantic annotation of sulco-gyral anatomy in mri images. this paper presents an interactive system for the annotation of brain anatomical structures in magnetic resonance images. the system is based on hybrid knowledge and techniques. first, it exploits both numerical knowledge from atlases and symbolic knowledge from a rule-extended ontology represented in owl, the web ontology language, and combines them with graphical data about cortical sulci, automatically extracted from the images. second, the annotations of the parts of gyri and of sulci located in a region of interest are obtained with different reasoning techniques: constraint satisfaction solving and description logics techniques. preliminary experiments have been achieved on normal and also pathological data. the results obtained so far are very promising.
a new and general method for blind shift-variant deconvolution of biomedical images. we present a new method for blind deconvolution of multiple noisy images blurred by a shift-variant point-spread-function (psf). we focus on a setting in which several images of the same object are available, and a transformation between these images is known. this setting occurs frequently in biomedical imaging, for example in microscopy or in medical ultrasound imaging. by using the information from multiple observations, we are able to improve the quality of images blurred by a shift-variant filter, without prior knowledge of this filter. also, in contrast to other work on blind and shift-variant deconvolution, in our approach no parametrization of the psf is required. we evaluate the proposed method quantitatively on synthetically degraded data as well as qualitatively on 3d ultrasound images of liver. the algorithm yields good restoration results and proves to be robust even in presence of high noise levels in the images.
null point imaging: a joint acquisition/analysis paradigm for mr classification. automatic classification of neurological tissues is a first step to many structural analysis pipelines. most computational approaches are designed to extract the best possible classification results out of mr data acquired with standard clinical protocols. we observe that the characteristics of the latter owe more to the historical circumstances under which they were developed and the visual appreciation of the radiographer who acquires the images than to the optimality with which they can be classified with an automatic algorithm. we submit that better performances could be obtained by considering the acquisition and analysis processes conjointly rather than optimising them independently. here, we propose such a joint approach to mr tissue classification in the form of a fast mr sequence, which nulls the magnitude and changes the sign of the phase at the boundary between tissue types. a simple phase-based thresholding algorithm then suffices to segment the tissues. preliminary results show promises to simplify and shorten the overall classification process.
robust computation of mutual information using spatially adaptive meshes. we present a new method for the fast and robust computation of information theoretic similarity measures for alignment of multi-modality medical images. the proposed method defines a non-uniform, adaptive sampling scheme for estimating the entropies of the images, which is less vulnerable to local maxima as compared to uniform and random sampling. the sampling is defined using an octree partition of the template image, and is preferable over other proposed methods of non-uniform sampling since it respects the underlying data distribution. it also extends naturally to a multi-resolution registration approach, which is commonly employed in the alignment of medical images. the effectiveness of the proposed method is demonstrated using both simulated mr images obtained from the brainweb database and clinical ct and spect images.
cutting tool system to minimize soft tissue damage for robot-assisted minimally invasive orthopedic surgery. minimally invasive surgery in orthopedic field is considered to be a challenging problem with a milling robot. one objective of this study is to minimize collision of the cutting tool with soft tissue. the authors have developed a robot with redundant axis to avoid the collision so far. some important components are modeled based on physical requirements, and a geometric optimization approach based on the model has been also proposed to improve performance. in this paper, a protective mechanism to cover the non-working part of the cutting edge is proposed to avoid soft tissue damage. hardware and software have been developed for this application and the effectiveness of this technique was evaluated with urethane bone.
extracting tractosemas from a displacement probability field for tractography in dw-mri. in this paper we present a novel method for estimating a field of asymmetric spherical functions, dubbed tractosemas, given the intra-voxel displacement probability information. the peaks of tractosemas correspond to directions of distinct fibers, which can have either symmetric or asymmetric local fiber structure. this is in contrast to the existing methods that estimate fiber orientation distributions which are naturally symmetric and therefore cannot model asymmetries such as splaying fibers. we propose a method for extracting tractosemas from a given field of displacement probability iso-surfaces via a diffusion process. the diffusion is performed by minimizing a kernel convolution integral, which leads to an update formula expressed in the convenient form of a discrete kernel convolution. the kernel expresses the probability of diffusion between two neighboring spherical functions and we model it by the product of gaussian and von mises distributions. the model is validated via experiments on synthetic and real diffusion-weighted magnetic resonance (dw-mri) datasets from a rat hippocampus and spinal cord.
automatic trajectory planning for deep brain stimulation: a feasibility study. dbs for parkinson's disease involves an extensive planning to find a suitable electrode implantation path to the selected target. we have investigated the feasibility of improving the conventional planning with an automatic calculation of possible paths in 3d. this requires the segmentation of anatomical structures. subsequently, the paths are calculated and visualized. after selection of a suitable path, the settings for the stereotactic frame are determined. a qualitative evaluation has shown that automatic avoidance of critical structures is feasible. the participating neurosurgeons estimate the time gain to be around 30 minutes.
geometric deformable model driven by cocrfs: application to optical coherence tomography. we present a geometric deformable model driven by dynamically updated probability fields. the shape is defined with the signed distance function, and the internal (smoothness) energy consists of a c 1 continuity constraint, a shape prior, and a term that forces the zero-level of the shape distance function towards a connected form. the image probability fields are estimated by our collaborative conditional random field (cocrf), which is updated during the evolution in an active learning manner: it infers class posteriors in pixels or regions with feature ambiguities by assessing the joint appearance of neighboring sites and using the classification confidence. we apply our method to optical coherence tomography fundus images for the segmentation of geographic atrophies in dry age-related macular degeneration of the human eye.
contributions to 3d diffeomorphic atlas estimation: application to brain images. this paper focuses on the estimation of statistical atlases of 3d images by means of diffeomorphic transformations. within a log-euclidean framework, the exponential and logarithm maps of diffeomorphisms need to be computed. in this framework, the inverse scaling and squaring (iss) method has been recently extended for the computation of the logarithm map, which is one of the most time demanding stages. in this work we propose to apply the baker-campbell-hausdorff (bch) formula instead. in a 3d simulation study, bch formula and iss method obtained similar accuracy but bch formula was more than 100 times faster. this approach allowed us to estimate a 3d statistical brain atlas in a reasonable time, including the average and the modes of variation. details for the computation of the modes of variation in the sobolev tangent space of diffeomorphisms are also provided.
ultrasound myocardial elastography and registered 3d tagged mri: quantitative strain comparison. ultrasound myocardial elastography (ume) and tagged magnetic resonance imaging (tmri) are two imaging modalities that were developed in the recent years to quantitatively estimate the myocardial deformations. tagged mri is currently considered as the gold standard for myocardial strain mapping in vivo. however, despite the low snr nature of ultrasound signals, echocardiography enjoys the widespread availability in the clinic, as well as its low cost and high temporal resolution. comparing the strain estimation performances of the two techniques has been of great interests to the community. in order to assess the cardiac deformation across different imaging modalities, in this paper, we developed a semi-automatic intensity and gradient based registration framework that rigidly registers the 3d tagged mris with the 2d ultrasound images. based on the two registered modalities, we conducted spatially and temporally more detailed quantitative strain comparison of the rf-based ume technique and tagged mri. from the experimental results, we conclude that qualitatively the two modalities share similar overall trends. but error and variations in ume accumulate over time. quantitatively tmri is more robust and accurate than ume.
diffuse parenchymal lung diseases: 3d automated detection in mdct. characterization and quantification of diffuse parenchymal lung disease (dpld) severity using mdct, mainly in interstitial lung diseases and emphysema, is an important issue in clinical research for the evaluation of new therapies. this paper develops a 3d automated approach for detection and diagnosis of dplds (emphysema, fibrosis, honeycombing, ground glass).the proposed methodology combines multiresolution image decomposition based on 3d morphological filtering, and graph-based classification for a full characterization of the parenchymal tissue. the very promising results obtained on a small patient database are good premises for a near implementation and validation of the proposed approach in clinical routine.
unsupervised reconstruction of a patient-specific surface model of a proximal femur from calibrated fluoroscopic images. in this paper, we present an unsupervised 2d/3d reconstruction scheme combining a parameterized multiple-component geometrical model and a point distribution model, and show its application to automatically reconstruct a surface model of a proximal femur from a limited number of calibrated fluoroscopic images with no user intervention at all. the parameterized multiple-component geometrical model is regarded as a simplified description capturing the geometrical features of a proximal femur. its parameters are optimally and automatically estimated from the input images using a particle filter based inference method. the estimated geometrical parameters are then used to initialize a point distribution model based 2d/3d reconstruction scheme for an accurate reconstruction of a surface model of the proximal femur. we designed and conducted in vitro and in vivo experiments to compare the present unsupervised reconstruction scheme to a supervised one. an average mean error of 1.2 mm was found when the supervised reconstruction scheme was used. it increased to 1.3 mm when the unsupervised one was used. however, the unsupervised reconstruction scheme has the advantage of elimination of user intervention, which holds the potential to facilitate the application of the 2d/3d reconstruction in surgical navigation.
sample sufficiency and number of modes to retain in statistical shape modelling. statistical shape modelling is a popular technique in medical imaging, but the issue of sample size sufficiency is not generally considered. also the number of principal modes retained is often chosen simply to cover a percentage of the total variance. we show that these simple rules are unreliable. we propose a new method that uses bootstrap replication and a t-test comparison with noise to decide whether each mode direction has stabilised. we establish mode correspondence by minimising the distance between the space spanned by the replicates and their mean. by retaining only stable modes, our method distinguishes real anatomical variation from modes dominated by random noise. this provides a lower stopping rule when the sample is small and converges as the sample size increases. we use this convergence to determine sample sufficiency. for validation we use synthetic datasets of the left ventricle generated with a known number of structural modes and added noise. our stopping rule detected the correct number of modes to retain where other methods failed. the methods were also tested on real 2d (22 points) and 3d (500 points) face data, retaining 24 and 70 modes with sample sufficiency being reached at approximately 50 and 150 samples respectively. for a 3d database of the left ventricle (527 points), 319 samples are not sufficient, but at this level we can retain around 55 stable modes. our method provides a principled foundation for appropriate selection of the number of modes to retain and determination of sample size sufficiency for statistical shape modelling.
cardiac-motion compensated mr imaging and strain analysis of ventricular trabeculae. in conventional cmr, bulk cardiac motion causes target structures to move in and out of the static acquisition plane. due to the partial volume effect, accurate localisation of subtle features through the cardiac cycle, such as the trabeculae and papillary muscles, is difficult. this problem is exacerbated by the short acquisition window necessary to avoid motion blur and ghosting, especially during early systole. this paper presents an adaptive imaging approach with comb multi-tag tracking that follows true 3d motion of the myocardium so that the same tissue slice is imaged throughout the cine acquisition. the technique is demonstrated with motion-compensated multi-slice imaging of ventricles, which allows for tracked visualisation and analysis of the trabeculae and papillary muscles for the first time. this enables novel in-vivo measurement of circumferential and radial strain for trabeculation and papillary muscle contractility. these statistics will facilitate the evaluation of diseases such as mitral valve insufficiency and ischemic heart disease. the adaptive imaging technique will also have significant implications for cmr in general, including motion-compensated quantification of myocardial perfusion and blood flow, and motion-correction of sequences with long acquisition windows.
high throughput analysis of breast cancer specimens on the grid. breast cancer accounts for about 30% of all cancers and 15% of all cancer deaths in women in the united states. advances in computer assisted diagnosis (cad) holds promise for early detecting and staging disease progression. in this paper we introduce a grid-enabled cad to perform automatic analysis of imaged histopathology breast tissue specimens. more than 100,000 digitized samples (1200 × 1200 pixels) have already been processed on the grid. we have analyzed results for 3744 breast tissue samples, which were originated from four different institutions using diaminobenzidine (dab) and hematoxylin staining. both linear and nonlinear dimension reduction techniques are compared, and the best one (isomap) was applied to reduce the dimensionality of the features. the experimental results show that the gentle boosting using an eight node cart decision tree as the weak learner provides the best result for classification. the algorithm has an accuracy of 86.02% using only 20% of the specimens as the training set.
spine segmentation using articulated shape models. including prior shape in the form of anatomical models is a well-known approach for improving segmentation results in medical images. currently, most approaches are focused on the modeling and segmentation of individual objects. in case of object constellations, a simultaneous segmentation of the ensemble that uses not only prior knowledge of individual shapes but also additional information about spatial relations between the objects is often beneficial. in this paper, we present a two-scale framework for the modeling and segmentation of the spine as an example for object constellations. the global spine shape is expressed as a consecution of local vertebra coordinate systems while individual vertebrae are modeled as triangulated surface meshes. adaptation is performed by attracting the model to image features but restricting the attraction to a former learned shape. with the developed approach, we obtained a segmentation accuracy of 1.0 mm in average for ten thoracic ct images improving former results.
on classifying disease-induced patterns in the brain using diffusion tensor images. diffusion tensor imaging (dti) provides rich information about brain tissue structure especially in the white matter, which is known to be affected in several diseases like schizophrenia. identifying patterns of brain changes induced by pathology is therefore crucial to clinical studies. however, the high dimensionality and complex structure of dti make it difficult to apply conventional linear statistical and pattern classification methods to identify such patterns. in this paper, we present a novel framework that uses a combination of dti-based anisotropy and geometry features to effectively identify brain regions with pathology-induced abnormality, and to classify brains into the diseased and healthy groups. our method first directly estimates the underlying overlap between the patient and control groups, based on a semi-parametric bayes error estimation method. by ranking voxels based on these estimation results, the method identifies abnormal brain regions from which features are extracted through kernel principal component analysis (kpca) for subsequent classification. application of the method to a dataset of controls and patients with schizophrenia, demonstrates promising accuracy of this framework in identifying brain patterns to separate two groups, and hence aiding in prognosis and treatment.
3d/2d image registration: the impact of x-ray views and their number. an important part of image-guided radiation therapy or surgery is registration of a three-dimensional (3d) preoperative image to two-dimensional (2d) images of the patient. it is expected that the accuracy and robustness of a 3d/2d image registration method do not depend solely on the registration method itself but also on the number and projections (views) of intraoperative images. in this study, we systematically investigate these factors by using registered image data, comprising of ct and x-ray images of a cadaveric lumbar spine phantom and the recently proposed 3d/2d registration method [1], [2]. the results indicate that the proportion of successful registrations (robustness) significantly increases when more x-ray images are used for registration.
automatic segmentation of blood vessels from dynamic mri datasets. in this paper we present an approach for blood vessel segmentation from dynamic contrast-enhanced mri datasets of the hand joints acquired from patients with active rheumatoid arthritis. exclusion of the blood vessels is needed for accurate visualisation of the activation events and objective evaluation of the degree of inflammation. the segmentation technique is based on statistical modelling motivated by the physiological properties of the individual tissues, such as speed of uptake and concentration of the contrast agent; it incorporates markov random field probabilistic framework and principal component analysis. the algorithm was tested on 60 temporal slices and has shown promising results.