waashk/dblp huggingface dataset:
a point-wise quantification of asymmetry using deformation fields: application to the study of the crouzon mouse model. this paper introduces a novel approach to quantify asymmetry in each point of a surface. the measure is based on analysing displacement vectors resulting from nonrigid image registration. a symmetric atlas, generated from control subjects is registered to a given subject image. a comparison of the resulting displacement vectors on the left and right side of the symmetry plane, gives a point-wise measure of asymmetry. the asymmetry measure was applied to the study of crouzon syndrome using micro ct scans of genetically modified mice. crouzon syndrome is characterised by the premature fusion of cranial sutures, which gives rise to a highly asymmetric growth. quantification and localisation of this asymmetry is of high value with respect to surgery planning and treatment evaluation. using the proposed method, asymmetry was calculated in each point of the surface of crouzon mice and wild-type mice (controls). asymmetry appeared in similar regions for the two groups but the crouzon mice were found significantly more asymmetric. the localisation ability of the method was in good agreement with ratings from a clinical expert. validating the quantification ability is a less trivial task due to the lack of a gold standard. nevertheless, a comparison with a different, but less accurate measure of asymmetry revealed good correlation.
automatic centerline extraction of irregular tubular structures using probability volumes from multiphoton imaging. in this paper, we present a general framework for extracting 3d centerlines from volumetric datasets. unlike the majority of previous approaches, we do not require a prior segmentation of the volume nor we do assume any particular tubular shape. centerline extraction is performed using a morphology-guided level set model. our approach consists of: i) learning the structural patterns of a tubular-like object, and ii) estimating the centerline of a tubular object as the path with minimal cost with respect to outward flux in gray level images. such shortest path is found by solving the eikonal equation. we compare the performance of our method with existing approaches in synthetic, ct, and multiphoton 3d images, obtaining substantial improvements, especially in the case of irregular tubular objects.
a hierarchical unsupervised spectral clustering scheme for detection of prostate cancer from magnetic resonance spectroscopy (mrs). magnetic resonance spectroscopy (mrs) along with mri has emerged as a promising tool in diagnosis and potentially screening for prostate cancer. surprisingly little work, however, has been done in the area of automated quantitative analysis of mrs data for identifying likely cancerous areas in the prostate. in this paper we present a novel approach that integrates a manifold learning scheme (spectral clustering) with an unsupervised hierarchical clustering algorithm to identify spectra corresponding to cancer on prostate mrs. ground truth location for cancer on prostate was determined from the sextant location and maximum size of cancer available from the acrin database, from where a total of 14 mrs studies were obtained. the high dimensional information in the mr spectra is non linearly transformed to a low dimensional embedding space and via repeated clustering of the voxels in this space, non informative spectra are eliminated and only informative spectra retained. our scheme successfully identified mrs cancer voxels with sensitivity of 77.8%, false positive rate of 28.92%, and false negative rate of 20.88% on a total of 14 prostate mrs studies. qualitative results seem to suggest that our method has higher specificity compared to a popular scheme, z-score, routinely used for analysis of mrs data.
a bayesian 3d volume reconstruction for confocal micro-rotation cell imaging. recently, micro-rotation confocal microscopy has enabled the acquisition of a sequence of slices for a non-adherent living cells where the slices' positions are roughly controlled by a dielectric-field biological cage. the high resolution volume reconstruction requires then the integration of precise alignment of slice positions. we propose in the bayesian context, a new method combining both slice positioning and 3d volume reconstruction simultaneously, which leads naturally to an energy minimization procedure of a variational problem. an automatic calibration paradigm via maximum likelihood estimation (mle) principle is used for the relative hyper-parameter determination. we provide finally experimental comparison results on both conventional z-stack confocal images and 3d volume reconstruction from micro-rotation slices of the same non-adherent living cell to show its potential biomedical application.
non-parametric diffeomorphic image registration with the demons algorithm. we propose a non-parametric diffeomorphic image registration algorithm based on thirion's demons algorithm. the demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. the main idea of our algorithm is to adapt this procedure to a space of diffeomorphic transformations. in contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of free form deformations by a few compositions. our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the true ones in terms of jacobians.
clinical evaluation of a respiratory gated guidance system for liver punctures. we have previously proposed a computer guidance system for liver punctures designed for intubated (free breathing) patients. the lack of accuracy reported (1 cm) was mostly due to the breathing motion that was not taken into account. in this paper we modify our system to synchronise the guidance information on the expiratory phases of the patient and present an evaluation on 6 patients of our respiratory gated system. firstly, we show how a specific choice of patient allows us to rigorously and passively evaluate the system accuracy. secondly, we demonstrate that our system can provide a guidance information with an error below 5 mm during expiratory phases.
precision targeting of liver lesions with a needle-based soft tissue navigation system. in this study, we assessed the targeting precision of a previously reported needle-based soft tissue navigation system. for this purpose, we implanted 10 2-ml agar nodules into three pig livers as tumor models, and two of the authors used the navigation system to target the center of gravity of each nodule. in order to obtain a realistic setting, we mounted the livers onto a respiratory liver motion simulator that models the human body. for each targeting procedure, we simulated the liver biopsy workflow, consisting of four steps: preparation, trajectory planning, registration, and navigation. the lesions were successfully hit in all 20 trials. the final distance between the applicator tip and the center of gravity of the lesion was determined from control computed tomography (ct) scans and was 3.5 ± 1.1 mm on average. robust targeting precision of this order of magnitude would significantly improve the clinical treatment standard for various ct-guided minimally invasive interventions in the liver.
combinatorial optimization for electrode labeling of eeg caps. an important issue in electroencephalographiy (eeg) experiments is to measure accurately the three dimensional (3d) positions of the electrodes. we propose a system where these positions are automatically estimated from several images using computer vision techniques. yet, only a set of undifferentiated points are recovered this way and remains the problem of labeling them, i.e. of finding which electrode corresponds to each point. this paper proposes a fast and robust solution to this latter problem based on combinatorial optimization. we design a specific energy that we minimize with a modified version of the loopy belief propagation algorithm. experiments on real data show that, with our method, a manual labeling of two or three electrodes only is sufficient to get the complete labeling of a 64 electrodes cap in less than 10 seconds.
mean template for tensor-based morphometry using deformation tensors. tensor-based morphometry (tbm) studies anatomical differences between brain images statistically, to identify regions that differ between groups, over time, or correlate with cognitive or clinical measures. using a nonlinear registration algorithm, all images are mapped to a common space, and statistics are most commonly performed on the jacobian determinant (local expansion factor) of the deformation fields. in [14], it was shown that the detection sensitivity of the standard tbm approach could be increased by using the full deformation tensors in a multivariate statistical analysis. here we set out to improve the common space itself, by choosing the shape that minimizes a natural metric on the deformation tensors from that space to the population of control subjects. this method avoids statistical bias and should ease nonlinear registration of new subjects data to a template that is 'closest' to all subjects' anatomies. as deformation tensors are symmetric positive-definite matrices and do not form a vector space, all computations are performed in the log-euclidean framework [1]. the control brain b that is already the closest to 'average' is found. a gradient descent algorithm is then used to perform the minimization that iteratively deforms this template and obtains the mean shape. we apply our method to map the profile of anatomical differences in a dataset of 26 hiv/aids patients and 14 controls, via a log-euclidean hotelling's t2 test on the deformation tensors. these results are compared to the ones found using the 'best' control, b. statistics on both shapes are evaluated using cumulative distribution functions of the p- values in maps of inter-group differences.
rapid voxel classification methodology for interactive 3d medical image visualization. in many medical imaging scenarios, real-time high-quality anatomical data visualization and interaction is important to the physician for meaningful diagnosis 3d medical data and get timely feedback. unfortunately, it is still difficult to achieve an optimized balance between real-time artifact-free medical image volume rendering and interactive data classification. in this paper, we present a new segment-based post color-attenuated classification algorithm to address this problem. in addition, we apply an efficient numerical integration computation technique and take advantage of the symmetric storage format of the color lookup table generation matrix. when implemented within our gpu-based volume raycasting system, the new classification technique is about 100 times faster than the unaccelerated pre-integrated classification approach, while achieving the similar or even superior quality volume rendered image. in addition, we propose an objective measure of artifacts in rendered medical image based on high-frequency spatial image content.
bayesian tracking of tubular structures and its application to carotid arteries in cta. this paper presents a bayesian framework for tracking of tubular structures such as vessels. compared to conventional tracking schemes, its main advantage is its non-deterministic character, which strongly increases the robustness of the method. a key element of our approach is a dedicated observation model for tubular structures in regions with varying intensities. furthermore, we show how the tracking method can be used to obtain a probabilistic segmentation of the tracked tubular structure. the method has been applied to track the internal carotid artery from ct angiography data of 14 patients (28 carotids) through the skull base. this is a challenging problem, owing to the close proximity of bone, overlap in intensity values of lumen voxels and (partial volume) bone voxels, and the tortuous path of the vessels. the tracking was successful in 25 cases, and the extracted path were found to be close (< 1.0mm) to manually traced paths by two observers.
similarity metrics for groupwise non-rigid registration. the use of groupwise registration techniques for average atlas construction has been a growing area of research in recent years. one particularly challenging component of groupwise registration is finding scalable and effective groupwise similarity metrics; these do not always extend easily from pairwise metrics. this paper investigates possible choices of similarity metrics and additionally proposes a novel metric based on normalised mutual information. the described groupwise metrics are quantitatively evaluated on simulated and 3d mr datasets, and their performance compared to equivalent pairwise registration.
2d motion analysis of long axis cardiac tagged mri. the tracking and reconstruction of myocardial motion is critical to the diagnosis and treatment of heart disease. currently, little has been done for the analysis of motion in long axis (la) cardiac images. we propose a new fully automated motion reconstruction method for grid- tagged mri that combines gabor filters and deformable models. first, we use a gabor filter bank to generate the corresponding phase map in the myocardium and estimate the location of grid tag intersections. second, we use a non-rigid registration module driven by thin plate splines (tps) to generate a transformation function between tag intersections in two consecutive images. third, deformable spline models are initialized using fourier domain analysis and tracked during the cardiac cycle using the tps generated transformation function. the splines will then locally deform under the influence of gradient flow and image phase information. the final motion is decomposed into tangential and normal components corresponding to the local orientation of the heart wall. the new method has been tested on la phantoms and in vivo heart data, and its performance has been quantitatively validated. the results show that our method can reconstruct the motion field in la cardiac tagged mr images accurately and efficiently.
shape-based myocardial contractility analysis using multivariate outlier detection. this paper presents a new approach to regional myocardial contractility analysis based on inter-landmark motion (ilm) vectors and multivariate outlier detection. the proposed spatio-temporal representation is used to describe the coupled changes occurring at pairs of regions of the left ventricle, thus enabling the detection of geometrical and dynamic inconsistencies. multivariate tolerance regions are derived from training samples to describe the variability within the normal population using the ilm vectors. for new left ventricular datasets, outlier detection enables the localization of extreme ilm observations and the corresponding myocardial abnormalities. the framework is validated on a relatively large sample of 50 subjects and the results show promise in localization and visualization of regional left ventricular dysfunctions.
evaluation of shape-based normalization in the corpus callosum for white matter connectivity analysis. recently, concerns have been raised that the correspondences computed by volumetric registration within homogeneous structures are primarily driven by regularization priors that differ among algorithms. this paper explores the correspondence based on geometric models for one of those structures, mid-sagittal section of the corpus callosum (mscc), and compared the result with registration paradigms. we use geometric model called continuous medial representation (cm-rep) to normalize anatomical structures on the basis of medial geometry, and use features derived from diffusion tensor tractography for validation. we show that shape-based normalization aligns subregions of the mscc, defined by connectivity, more accurately than normalization based on volumetric registration. furthermore, shape-based normalization helps increase the statistical power of group analysis in an experiment where features derived from diffusion tensor tractography are compared between two cohorts. these results suggest that cm-rep is an appropriate tool for normalizing the mscc in white matter studies.
three-dimensional ultrasound mosaicing. the creation of 2d ultrasound mosaics is becoming a common clinical practice with a high clinical value. the next step coming along with the increasing availability of 2d array transducers is the creation of 3d mosaics. in the literature of ultrasound registration, the alignment of multiple images has not yet been addressed. therefore, we propose registration strategies, which are able to cope with problems arising by multiple image alignment. among others, we use simultaneous registration which urges the usage of multivariate similarity measures. in this paper, we propose alternative multivariate extensions based on a maximum likelihood framework. experimental results show the good performance of the proposed registration strategies and similarity measures.
variational guidewire tracking using phase congruency. we present a novel method to track a guidewire in cardiac x-ray video. using variational calculus, we derive differential equations that deform a spline, subject to intrinsic and extrinsic forces, so that it matches the image data, remains smooth, and preserves an a priori length. we analytically derive these equations from first principles, and show how they include tangential terms, which we include in our model. to address the poor contrast often observed in x-ray video, we propose using phase congruency as an image-based feature. experimental results demonstrate the success of the method in tracking guidewires in low contrast x-ray video.
efficient selection of the most similar image in a database for critical structures segmentation. radiotherapy planning needs accurate delineations of the critical structures. atlas-based segmentation has been shown to be very efficient to delineate brain structures [1]. however, the construction of an atlas from a dataset of images [2], particularly for the head and neck region, is very difficult due to the high variability of the images and can generate over-segmented structures in the atlas. to overcome this drawback, we present in this paper an alternative method to select as a template the image in a database that is the most similar to the patient to be segmented. this similarity is based on a distance between transformations. a major contribution is that we do not compute every patient-to-sample registration to find the most similar template, but only the registration of the patient towards an average image. this method has therefore the advantage of being computationally very efficient. we present a qualitative and quantitative comparison between the proposed method and a classical atlas-based segmentation method. this evaluation is performed on a subset of 45 patients using a leave-one-out method and shows a great improvement of the specificity of the results.
detecting mechanical abnormalities in prostate tissue using fe-based image registration. an image registration-based elastography algorithm is presented for assessing the stiffness of tissue regions inside the prostate for the purpose of detecting tumors. a 3d finite-element model of the prostate is built from ultrasound images and used to simulate the deformation of the prostate induced by a trus probe. to reconstruct the stiffness of tissues, their young's moduli are varied using powell's method so that the mutual information between a simulated and deformed image volume is maximized. the algorithm was validated using a gelatin prostate phantom embedded with a cylindrical inclusion that simulated a tumor. results from the phantom study showed that the technique could detect the increased stiffness of the simulated tumor with a reasonable accuracy.
automated extraction of lymph nodes from 3-d abdominal ct images using 3-d minimum directional difference filter. this paper presents a method for extracting lymph node regions from 3-d abdominal ct images using 3-d minimum directional difference filter. in the case of surgery of colonic cancer, resection of metastasis lesions is performed with resection of a primary lesion. lymph nodes are main route of metastasis and are quite important for deciding resection area. diagnosis of enlarged lymph nodes is quite important process for surgical planning. however, manual detection of enlarged lymph nodes on ct images is quite burden task. thus, development of lymph node detection process is very helpful for assisting such surgical planning task. although there are several report that present lymph node detection, these methods detect lymph nodes primary from pet images or detect in 2-d image processing way. there is no method that detects lymph nodes directly from 3-d images. the purpose of this paper is to show an automated method for detecting lymph nodes from 3-d abdominal ct images. this method employs a 3-d minimum directional difference filter for enhancing blob structures with suppressing line structures. after that, false positive regions caused by residua and vein are eliminated using several kinds of information such as size, blood vessels, air in the colon. we applied the proposed method to three cases of 3-d abdominal ct images. the experimental results showed that the proposed method could detect 57.0 % of enlarged lymph nodes with 58 fps per case.
quality-based registration and reconstruction of optical tomography volumes. ultramicroscopy, a novel optical tomographic imaging modality related to fluorescence microscopy, allows to acquire cross-sectional slices of small specially prepared biological samples with astounding quality and resolution. however, scattering of the fluorescence light causes the quality to decrease proportional to the depth of the currently imaged plane. scattering and beam thickness of the excitation laser light cause additional image degradation. we perform a physical simulation of the light scattering in order to define a quantitative function of image quality with respect to depth. this allows us to establish 3d-volumes of quality information in addition to the image data. volumes are acquired at different orientations of the sample, hence providing complementary regions of high quality. we propose an algorithm for rigid 3d-3d registration of these volumes incorporating voxel quality information, based onmaximizing an adapted linear correlation term. the quality ratio of the images is then used, along with the registration result, to create improved volumes of the imaged object. the methods are applied on acquisitions of a mouse brain and mouse embryo to create outstanding three-dimensional reconstructions.
real-time fusion of ultrasound and gamma probe for navigated localization of liver metastases. liver metastases are an advanced stage of several types of cancer, usually treated with surgery. intra-operative localization of these lesions is currently facilitated by intra-operative ultrasound (ious) and palpation, yielding a high rate of false positives due to benign abnormal regions. in this paper we present the integration of functional nuclear information from a gamma probe with ious, to provide a synchronized, real-time visualization that facilitates the detection of active metastases intra-operatively. we evaluate the system in an ex-vivo setup employing a group of physicians and medical technicians and show that the addition of functional imaging improves the accuracy of localizing and identifying malignant and benign lesions significantly. furthermore we are able to demonstrate that the inclusion of an advanced, augmented visualization provides more reliability and confidence on classifying these lesions in the presented evaluation setup.