waashk/dblp huggingface dataset:
automated anatomical labeling of bronchial branches extracted from ct datasets based on machine learning and combination optimization and its application to bronchoscope guidance. this paper presents a method for the automated anatomical labeling of bronchial branches extracted from 3d ct images based on machine learning and combination optimization. we also show applications of anatomical labeling on a bronchoscopy guidance system. this paper performs automated labeling by using machine learning and combination optimization. the actual procedure consists of four steps: (a) extraction of tree structures of the bronchus regions extracted from ct images, (b) construction of adaboost classifiers, (c) computation of candidate names for all branches by using the classifiers, (d) selection of best combination of anatomical names. we applied the proposed method to 90 cases of 3d ct datasets. the experimental results showed that the proposed method can assign correct anatomical names to 86.9% of the bronchial branches up to the sub-segmental lobe branches. also, we overlaid the anatomical names of bronchial branches on real bronchoscopic views to guide real bronchoscopy.
heart motion abnormality detection via an information measure and bayesian filtering. this study investigates heart wall motion abnormality detection with an information theoretic measure of heart motion based on the shannon's differential entropy (sde) and recursive bayesian filtering. heart wall motion is generally analyzed using functional images which are subject to noise and segmentation inaccuracies, and incorporation of prior knowledge is crucial in improving the accuracy. the kalman filter, a well known recursive bayesian filter, is used in this study to estimate the left ventricular (lv) cavity points given incomplete and noisy data, and given a dynamic model. however, due to similarities between the statistical information of normal and abnormal heart motions, detecting and classifying abnormality is a challenging problem which we proposed to investigate with a global measure based on the sde. we further derive two other possible information theoretic abnormality detection criteria, one is based on rényi entropy and the other on fisher information. the proposed method analyzes wall motion quantitatively by constructing distributions of the normalized radial distance estimates of the lv cavity. using 269×20 segmented lv cavities of short-axis magnetic resonance images obtained from 30 subjects, the experimental analysis demonstrates that the proposed sde criterion can lead to significant improvement over other features that are prevalent in the literature related to the lv cavity, namely, mean radial displacement and mean radial velocity.
evaluation of -space sampling strategies for the diffusion magnetic resonance imaging. we address the problem of efficient sampling of the diffusion space for the diffusion magnetic resonance imaging (dmri) modality. while recent scanner improvements enable the acquisition of more and more detailed images, it is still unclear which q-space sampling strategy gives the best performance. we evaluate several q-space sampling distributions by an approach based on the approximation of the mr signal by a series expansion of spherical harmonics and laguerre-gaussian functions. with the help of synthetic experiments, we identify a subset of sampling distributions which leads to the best reconstructed data.
vibro-elastography for visualization of the prostate region: method evaluation. we show that vibro-elastography, an ultrasound-based method that creates images of tissue viscoelasticity contrast, can be used for visualization and segmentation of the prostate. we use mri as the gold standard and show that ve images yield more accurate 3d volumes of the prostate gland than conventional b-mode imaging. furthermore, we propose two novel measures characterizing the strength and continuity of edges in noisy images. these measures, as well as contrast to noise ratio, demonstrate the utility of ve as a prostate imaging modality. the results of our study show that in addition to mapping the visco-elastic properties of tissue, ve can play a central role in improving the anatomic visualization of the prostate region and become an integral component of interventional procedures such as brachytherapy.
interactive simulation of flexible needle insertions based on constraint models. this paper presents a new modeling method for the insertion of needles and more generally thin and flexible medical devices into soft tissues. several medical procedures rely on the insertion of slender medical devices such as biopsy, brachytherapy, deep-brain stimulation. in this paper, the interactions between soft tissues and flexible instruments are reproduced using a set of dedicated complementarity constraints. each constraint is positionned and applied to the deformable models without requiring any remeshing. our method allows for the 3d simulation of different physical phenomena such as puncture, cutting, static and dynamic friction at interactive frame rate. to obtain realistic simulation, the model can be parametrized using experimental data. our method is validated through a series of typical simulation examples and new more complex scenarios.
multimodal prior appearance models based on regional clustering of intensity profiles. model-based image segmentation requires prior information about the appearance of a structure in the image. instead of relying on principal component analysis such as in statistical appearance models, we propose a method based on a regional clustering of intensity profiles that does not rely on an accurate pointwise registration. our method is built upon the expectation-maximization algorithm with regularized covariance matrices and includes spatial regularization. the number of appearance regions is determined by a novel model order selection criterion. the prior is described on a reference mesh where each vertex has a probability to belong to several intensity profile classes.
dynamic layer separation for coronary dsa and enhancement in fluoroscopic sequences. this paper presents a new technique of coronary digital subtraction angiography which separates layers of moving background structures from dynamic fluoroscopic sequences of the heart and obtains moving layers of coronary arteries. a bayeisan framework combines dense motion estimation, uncertainty propagation and statistical fusion to achieve reliable background layer estimation and motion compensation for coronary sequences. encouraging results have been achieved on clinically acquired coronary sequences, where the proposed method considerably improves the visibility and perceptibility of coronary arteries undergoing breathing and cardiac movements. perceptibility improvement is significant especially for very thin vessels. clinical benefit is expected in the context of obese patients and deep angulation, as well as in the reduction of contrast dose in normal size patients.
analysis of mr images of mice in preclinical treatment monitoring of polycystic kidney disease. a common cause of kidney failure is autosomal dominant polycystic kidney disease (adpkd). it is characterized by the growth of cysts in the kidneys and hence the growth of the entire kidneys with eventual failure in most cases by age 50. no preventive treatment for this condition is available. preclinical drug treatment studies use an in vivo mouse model of the condition. the analysis of mice imaging data for such studies typically requires extensive manual interaction, which is subjective and not reproducible. in this work both untreated and treated mice have been imaged with a high field, 9.4t, mri animal scanner and a reliable algorithm for the automated segmentation of the mouse kidneys has been developed. the algorithm first detects the region of interest (roi) in the image surrounding the kidneys. a parameterized geometric shape for a kidney is registered to the roi of each kidney. the registered shapes are incorporated as priors to the graph cuts algorithm used to extract the kidneys. the accuracy of the automated segmentation has been demonstrated by comparing it with a manual segmentation. the processing results are also consistent with the literature for previous techniques.
discriminative, semantic segmentation of brain tissue in mr images. a new algorithm is presented for the automatic segmentation and classification of brain tissue from 3d mr scans. it uses discriminative random decision forest classification and takes into account partial volume effects. this is combined with correction of intensities for the mr bias field, in conjunction with a learned model of spatial context, to achieve accurate voxel-wise classification. our quantitative validation, carried out on existing labelled datasets, demonstrates improved results over the state of the art, especially for the cerebro-spinal fluid class which is the most difficult to label accurately.
a computer-aided diagnosis system of nuclear cataract via ranking. a novel computer-aided diagnosis system of nuclear cataract via ranking is firstly proposed in this paper. the grade of nuclear cataract in a slit-lamp image is predicted based on its neighboring labeled images in a ranked images list, which is achieved using an optimal ranking function. a new ranking evaluation measure is proposed for learning the optimal ranking function via direct optimization. our system has been tested by a large dataset composed of 1000 slit-lamp images from 1000 different cases. both experimental results and comparison with several state-of-the-art methods indicate the superiority of our system.
septal flash assessment on crt candidates based on statistical atlases of motion. in this paper, we propose a complete framework for the automatic detection and quantification of abnormal heart motion patterns using statistical atlases of motion built from healthy populations. the method is illustrated on crt patients with identified cardiac dyssynchrony and abnormal septal motion on 2d ultrasound (us) sequences. the use of the 2d us modality guarantees that the temporal resolution of the image sequences is high enough to work under a small displacements hypothesis. under this assumption, the computed displacement fields can be directly considered as cardiac velocities. comparison of subjects acquired with different spatiotemporal resolutions implies the reorientation and temporal normalization of velocity fields in a common space of coordinates. statistics are then performed on the reoriented vector fields. results show the ability of the method to correctly detect abnormal motion patterns and quantify their distance to normality. the use of local p-values for quantifying abnormal motion patterns is believed to be a promising strategy for computing new markers of cardiac dyssynchrony for better characterizing crt candidates.
a fuzzy region-based hidden markov model for partial-volume classification in brain mri. we present a novel fuzzy region-based hidden markov model (frbhmm) for unsupervised partial-volume classification in brain magnetic resonance images (mris). the primary contribution is an efficient graphical representation of 3d image data in which irregularly-shaped image regions have memberships to a number of classes rather than one discrete class. our model groups voxels into regions for efficient processing, but also refines the region boundaries to the voxel level for optimal accuracy. this strategy is most effective in data where partial-volume effects due to resolution-limited image acquisition result in intensity ambiguities. our frbhmm employs a forward-backward scheme for parameter estimation through iterative computation of region class likelihoods. we validate our proposed method on simulated and clinical brain mris of both normal and multiple sclerosis subjects. quantitative results demonstrate the advantages of our fuzzy model over the discrete approach with significant improvements in classification accuracy (30% reduction in mean square error).
optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate. we present a novel method for globally optimal surface segmentation of multiple mutually interacting objects, incorporating both edge and shape knowledge in a 3-d graph-theoretic approach. hard surface interacting constraints are enforced in the interacting regions, preserving the geometric relationship of those partially interacting surfaces. the soft smoothness a priori shape compliance is introduced into the energy functional to provide shape guidance. the globally optimal surfaces can be simultaneously achieved by solving a maximum flow problem based on an arc-weighted graph representation. representing the segmentation problem in an arc-weighted graph, one can incorporate a wider spectrum of constraints into the formulation, thus increasing segmentation accuracy and robustness in volumetric image data. to the best of our knowledge, our method is the first attempt to introduce the arc-weighted graph representation into the graph-searching approach for simultaneous segmentation of multiple partially interacting objects, which admits a globally optimal solution in a low-order polynomial time. our new approach was applied to the simultaneous surface detection of bladder and prostate. the result was quite encouraging in spite of the low saliency of the bladder and prostate in ct images.
multiple sclerosis lesion segmentation using an automatic multimodal graph cuts. graph cuts have been shown as a powerful interactive segmentation technique in several medical domains. we propose to automate the graph cuts in order to automatically segment multiple sclerosis (ms) lesions in mri. we replace the manual interaction with a robust em-based approach in order to discriminate between ms lesions and the normal appearing brain tissues (nabt). evaluation is performed in synthetic and real images showing good agreement between the automatic segmentation and the target segmentation. we compare our algorithm with the state of the art techniques and with several manual segmentations. an advantage of our algorithm over previously published ones is the possibility to semi-automatically improve the segmentation due to the graph cuts interactive feature.
3d medical image segmentation by multiple-surface active volume models. in this paper, we propose multiple-surface active volume models (msavm) to extract 3d objects from volumetric medical images. being able to incorporate spatial constraints among multiple objects, msavm is more robust and accurate than the original active volume models [1]. the main novelty in msavm is that it has two surface-distance based functions to adaptively adjust the weights of contribution from the image-based region information and from spatial constraints among multiple interacting surfaces. these two functions help msavm not only overcome local minima but also avoid leakage. because of the implicit representation of avm, the spatial information can be calculated based on the model's signed distance transform map with very low extra computational cost. the msavm thus has the efficiency of the original 3d avm but produces more accurate results. 3d segmentation results, validation and comparison are presented for experiments on volumetric medical images.
mapping growth patterns and genetic influences on early brain development in twins. despite substantial progress in understanding the anatomical and functional development of the human brain, little is known on the spatial-temporal patterns and genetic influences on white matter maturation in twins. neuroimaging data acquired from longitudinal twin studies provide a unique platform for scientists to investigate such issues. however, the interpretation of neuroimaging data from longitudinal twin studies is hindered by the lacking of appropriate image processing and statistical tools. in this study, we developed a statistical framework for analyzing longitudinal twin neuroimaging data, which is consisted of generalized estimating equation (gee2) and a test procedure. the gee2 method can jointly model imaging measures with genetic effect, environmental effect, and behavioral and clinical variables. the score test statistic is used to test linear hypothesis such as the association between brain structure and function with the covariates of interest. a resampling method is used to control the family-wise error rate to adjust for multiple comparisons. with diffusion tensor imaging (dti), we demonstrate the application of our statistical methods in quantifying the spatiotemporal white matter maturation patterns and in detecting the genetic effects in a longitudinal neonatal twin study. the proposed approach can be easily applied to longitudinal twin data with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number of measurements.
combining registration and minimum surfaces for the segmentation of the left ventricle in cardiac cine mr images. this paper describes a system to automatically segment the left ventricle in all slices and all phases of cardiac cine magnetic resonance datasets. after localizing the left ventricle blood pool using motion, thresholding and clustering, slices are segmented sequentially. for each slice, deformable registration is used to align all the phases, candidates contours are recovered in the average image using shortest paths, and a minimal surface is built to generate the final contours. the advantage of our method is that the resulting contours follow the edges in each phase and are consistent over time. we demonstrate using 19 patient examples that the results are very good. the rms distance between ground truth and our segmentation is only 1.6 pixels (2.7 mm) and the dice coefficient is 0.89.
utero-fetal unit and pregnant woman modeling using a computer graphics approach for dosimetry studies. potential sanitary effects related to electromagnetic fields exposure raise public concerns, especially for fetuses during pregnancy. human fetus exposure can only be assessed through simulated dosimetry studies, performed on anthropomorphic models of pregnant women. in this paper, we propose a new methodology to generate a set of detailed utero-fetal unit (ufu) 3d models during the first and third trimesters of pregnancy, based on segmented 3d ultrasound and mri data. ufu models are built using recent geometry processing methods derived from mesh-based computer graphics techniques and embedded in a synthetic woman body. nine pregnant woman models have been generated using this approach and validated by obstetricians, for anatomical accuracy and representativeness.
statistical location model for abdominal organ localization. initial placement of the models is an essential pre-processing step for model-based organ segmentation. based on the observation that organs move along with the spine and their relative locations remain relatively stable, we built a statistical location model (slm) and applied it to abdominal organ localization. the model is a point distribution model which learns the pattern of variability of organ locations relative to the spinal column from a training set of normal individuals. the localization is achieved in three stages: spine alignment, model optimization and location refinement. the slm is optimized through maximum a posteriori estimation of a probabilistic density model constructed for each organ. our model includes five organs: liver, left kidney, right kidney, spleen and pancreas. we validated our method on 12 abdominal cts using leave-one-out experiments. the slm enabled reduction in the overall localization error from 62.0±28.5 mm to 5.8±1.5 mm. experiments showed that the slm was robust to the reference model selection.
functional segmentation of fmri data using adaptive non-negative sparse pca (anspca). we propose a novel method for functional segmentation of fmri data that incorporates multiple functional attributes such as activation effects and functional connectivity, under a single framework. similar to pca, our method exploits the structure of the correlation matrix but with neighborhood information adaptively integrated to encourage detection of spatially contiguous clusters yet without falsely pooling non-active voxels near the functional boundaries. in addition, our method adaptively combines pca and replicator dynamics, which we show to be equivalent to non-negative sparse pca, based on the sparsity of the activation pattern. we validate our method quantitatively on synthetic data and demonstrate that it outperforms methods including replicator dynamics, pca, gaussian mixture models, and general linear models. furthermore, when applied to real fmri data, our method successfully segmented the brodmann area 6 into its known functional sub-regions, whereas other conventional methods that we examined failed to attain such delineation.
a tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features. diffusion tensor imaging plays a key role in our understanding of white matter (wm) both in normal populations and in populations with brain disorders. existing techniques focus primarily on using diffusivity-based quantities derived from diffusion tensor as surrogate measures of microstructural tissue properties of wm. in this paper, we describe a novel tract-specific framework that enables the examination of wm morphometry at both the macroscopic and microscopic scales. the framework leverages the skeleton-based modeling of sheet-like wm fasciculi using the continuous medial representation, which gives a natural definition of thickness and supports its comparison across subjects. the thickness measure provides a macroscopic characterization of wm fasciculi that complements existing analysis of microstructural features. the utility of the framework is demonstrated in quantifying wm atrophy in amyotrophic lateral sclerosis, a severe neurodegenerative disease of motor neurons. we show that, compared to using microscopic features alone, combining the macroscopic and microscopic features gives a more holistic characterization of the disease.
topological correction of brain surface meshes using spherical harmonics. a brain surface reconstruction allows advanced analysis of structural and functional brain data that is not possible using volumetric data alone. however, the generation of a brain surface mesh from mri data often introduces topological defects and artifacts that must be corrected. we show that it is possible to accurately correct these errors using spherical harmonics. our results clearly demonstrate that brain surface meshes reconstructed using spherical harmonics are free from topological defects and large artifacts that were present in the uncorrected brain surface. visual inspection reveals that the corrected surfaces are of very high quality. the spherical harmonic surfaces are also quantitatively validated by comparing the surfaces to an "ideal" brain based on a manually corrected average of twelve scans of the same subject. in conclusion, the spherical harmonics approach is a direct, computationally fast method to correct topological errors.
a two-level approach towards semantic colon segmentation: removing extra-colonic findings. computer aided detection (cad) of colonic polyps in computed tomographic colonography has tremendously impacted colorectal cancer diagnosis using 3d medical imaging. it is a prerequisite for all cad systems to extract the air-distended colon segments from 3d abdomen computed tomography scans. in this paper, we present a two-level statistical approach of first separating colon segments from small intestine, stomach and other extra-colonic parts by classification on a new geometric feature set; then evaluating the overall performance confidence using distance and geometry statistics over patients. the proposed method is fully automatic and validated using both the classification results in the first level and its numerical impacts on false positive reduction of extra-colonic findings in a cad system. it shows superior performance than the state-of-art knowledge or anatomy based colon segmentation algorithms [1,2,3].