waashk/dblp huggingface dataset:
3d cardiac segmentation using temporal correlation of radio frequency ultrasound data. semi-automatic segmentation of the myocardium in 3d echographic images may substantially support clinical diagnosis of heart disease. particularly in children with congenital heart disease, segmentation should be based on the echo features solely since a priori knowledge on the shape of the heart cannot be used. segmentation of echocardiographic images is challenging because of the poor echogenicity contrast between blood and the myocardium in some regions and the inherent speckle noise from randomly backscattered echoes. phase information present in the radio frequency (rf) ultrasound data might yield useful, additional features in these regions. a semi-3d technique was used to determine maximum temporal cross-correlation values locally from the rf data. to segment the endocardial surface, maximum cross-correlation values were used as additional external force in a deformable model approach and were tested against and combined with adaptive filtered, demodulated rf data. the method was tested on full volume images (philips, ie33) of four healthy children and evaluated by comparison with contours obtained from manual segmentation.
automated segmentation of the femur and pelvis from 3d ct data of diseased hip using hierarchical statistical shape model of joint structure. segmentation of the femur and pelvis from 3d data is prerequisite of patient specific planning and simulation for hip surgery. separation of the femoral head and acetabulum is one of main difficulties in the diseased hip joint due to deformed shapes and extreme narrowness of the joint space. in this paper, we develop a hierarchical multi-object statistical shape model representing joint structure for automated segmentation of the diseased hip from 3d ct images. in order to represent shape variations as well as pose variations of the femur against the pelvis, both shape and pose variations are embedded in a combined pelvis and femur statistical shape model (ssm). further, the whole combined ssm is divided into individual pelvis and femur ssms and a partial combined ssm only including the acetabulum and proximal femur. the partial combined ssm maintains the consistency of the two bones by imposing the constraint that the shapes of the overlapped portions of the individual and partial combined ssms are identical. the experimental results show that segmentation and separation accuracy of the femur and pelvis was improved using the proposed method compared with independent use of the pelvis and femur ssms.
lung extraction, lobe segmentation and hierarchical region assessment for quantitative analysis on high resolution computed tomography images. regional assessment of lung disease (such as chronic obstructive pulmonary disease) is a critical component to accurate patient diagnosis. software tools than enable such analysis are also important for clinical research studies. in this work, we present an image segmentation and data representation framework that enables quantitative analysis specific to different lung regions on high resolution computed tomography (hrct) datasets. we present an offline, fully automatic image processing chain that generates airway, vessel, and lung mask segmentations in which the left and right lung are delineated. we describe a novel lung lobe segmentation tool that produces reproducible results with minimal user interaction. a usability study performed across twenty datasets (inspiratory and expiratory exams including a range of disease states) demonstrates the tool's ability to generate results within five to seven minutes on average. we also describe a data representation scheme that involves compact encoding of label maps such that both "regions" (such as lung lobes) and "types" (such as emphysematous parenchyma ) can be simultaneously represented at a given location in the hrct.
dynamic cone beam reconstruction using a new level set formulation. this paper addresses an approach toward tomographic reconstruction from rotational angiography data as it is generated by c-arms in cardiac imaging. since the rotational acquisition scheme forces a trade-off between consistency of the scene and reasonable baselines, most existing reconstruction techniques fail at recovering the 3d+t scene.we propose a new reconstruction framework based on variational level sets including a new data term for symbolic reconstruction as well as a novel incorporation of motion into the level set formalism. the resulting simultaneous estimation of shape and motion proves feasible in the presented experiments. since the proposed formulation offers a great flexibility in incorporating other data terms as well as hard or soft constraints, it allows an adaption to a wider range of problems and could be of interest to other reconstruction settings as well.
a new 3-d automated computational method to evaluate in-stent neointimal hyperplasia in in-vivo intravascular optical coherence tomography pullbacks. detection of stent struts imaged in vivo by optical coherence tomography (oct) after percutaneous coronary interventions (pci) and quantification of in-stent neointimal hyperplasia (nih) are important. in this paper, we present a new computational method to facilitate the physician in this endeavor to assess and compare new (drug-eluting) stents. we developed a new algorithm for stent strut detection and utilized splines to reconstruct the lumen and stent boundaries which provide automatic measurements of nih thickness, lumen and stent area. our original approach is based on the detection of stent struts unique characteristics: bright reflection and shadow behind. furthermore, we present for the first time to our knowledge a rotation correction method applied across oct cross-section images for 3d reconstruction and visualization of reconstructed lumen and stent boundaries for further analysis in the longitudinal dimension of the coronary artery. our experiments over oct cross-sections taken from 7 patients presenting varying degrees of nih after pci illustrate a good agreement between the computer method and expert evaluations: bland-altmann analysis revealed a mean difference for lumen cross-section area of 0.11±0.70mm 2 and for the stent cross-section area of 0.10±1.28mm 2.
real-time prediction of brain shift using nonlinear finite element algorithms. patient-specific biomechanical models implemented using specialized nonlinear (i.e. taking into account material and geometric nonlinearities) finite element procedures were applied to predict the deformation field within the brain for five cases of craniotomy-induced brain shift. the procedures utilize the total lagrangian formulation with explicit time stepping. the loading was defined by prescribing deformations on the brain surface under the craniotomy. application of the computed deformation fields to register the preoperative images with the intraoperative ones indicated that the models very accurately predict the intraoperative positions and deformations of the brain anatomical structures for limited information about the brain surface deformations. for each case, it took less than 40 s to compute the deformation field using a standard personal computer, and less than 4 s using a graphics processing unit (gpu). the results suggest that nonlinear biomechanical models can be regarded as one possible method of complementing medical image processing techniques when conducting non-rigid registration within the real-time constraints of neurosurgery.
surgical planning and patient-specific biomechanical simulation for tracheal endoprostheses interventions. we have developed a system for computer-assisted surgical planning of tracheal surgeries. the system allows to plan the intervention based on ct images of the patient, and includes a virtual database of commercially available prostheses. automatic segmentation of the trachea and apparent pathological structures is obtained using a modified region growing algorithm. a method for automatic adaptation of a finite element mesh allows to build a patient-specific biomechanical model for simulation of the expected performance of the implant under physiological movement (swallowing, sneezing). laboratory experiments were performed to characterise the tissues present in the trachea, and movement models were obtained from fluoroscopic images of a patient. results are reported on the planning and biomechanical simulation of two patients that underwent surgery at our hospital.
unsupervised inline analysis of cardiac perfusion mri. in this paper we first discuss the technical challenges preventing an automated analysis of cardiac perfusion mr images and subsequently present a fully unsupervised workflow to address the problems. the proposed solution consists of key-frame detection, consecutive motion compensation, surface coil inhomogeneity correction using proton density images and robust generation of pixel-wise perfusion parameter maps. the entire processing chain has been implemented on clinical mr systems to achieve unsupervised inline analysis of perfusion mri. validation results are reported for 260 perfusion time series, demonstrating feasibility of the approach.
tensor-based morphometry of fibrous structures with application to human brain white matter. tensor-based morphometry (tbm) is a powerful approach for examining shape changes in anatomy both across populations and in time. our work extends the standard tbm for quantifying local volumetric changes to establish both rich and intuitive descriptors of shape changes in fibrous structures. it leverages the data from diffusion tensor imaging to determine local spatial configuration of fibrous structures and combines this information with spatial transformations derived from image registration to quantify fibrous structure-specific changes, such as local changes in fiber length and in thickness of fiber bundles. in this paper, we describe the theoretical framework of our approach in detail and illustrate its application to study brain white matter. our results show that additional insights can be gained with the proposed analysis.
automatic extraction of mandibular nerve and bone from cone-beam ct data. the exact localization of the mandibular nerve with respect to the bone is important for applications in dental implantology and maxillofacial surgery. cone beam computed tomography (cbct), often also called digital volume tomography (dvt), is increasingly utilized in maxillofacial or dental imaging. compared to conventional ct, however, soft tissue discrimination is worse due to a reduced dose. thus, small structures like the alveolar nerves are even harder recognizable within the image data. we show that it is nonetheless possible to accurately reconstruct the 3d bone surface and the course of the nerve in a fully automatic fashion, with a method that is based on a combined statistical shape model of the nerve and the bone and a dijkstra-based optimization procedure. our method has been validated on 106 clinical datasets: the average reconstruction error for the bone is 0.5±0.1 mm, and the nerve can be detected with an average error of 1.0±0.6 mm.
shape modelling for tract selection. probabilistic tractography provides estimates of the probability of a structural connection between points or regions in a brain volume, based on information from diffusion mri. the ability to estimate the uncertainty associated with reconstructed pathways is valuable, but noise in the image data leads to premature termination or erroneous trajectories in sampled streamlines. in this work we describe automated methods, based on a probabilistic model of tract shape variability between individuals, which can be applied to select seed points in order to maximise consistency in tract segmentation; and to discard streamlines which are unlikely to belong to the tract of interest. our method is shown to ameliorate false positives and remove the widely observed falloff in connection probability with distance from the seed region due to noise, two important problems in the tractography literature. moreover, the need to apply an arbitrary threshold to connection probability maps is entirely obviated by our approach, thus removing a significant user-specified parameter from the tractography pipeline.
respiratory motion estimation from cone-beam projections using a prior model. respiratory motion introduces uncertainties when planning and delivering radiotherapy for lung cancer patients. cone-beam projections acquired in the treatment room could provide valuable information for building motion models, useful for gated treatment delivery or motion compensated reconstruction. we propose a method for estimating 3d+t respiratory motion from the 2d+t cone-beam projection sequence by including prior knowledge about the patient's breathing motion. motion estimation is accomplished by maximizing the similarity of the projected view of a patient specific model to observed projections of the cone-beam sequence. this is done semi-globally, considering entire breathing cycles. using realistic patient data, we show that the method is capable of good prediction of the internal patient motion from cone-beam data, even when confronted with interfractional changes in the breathing motion.
volumetric shape model for oriented tubular structure from dti data. in this paper, we describe methods for constructing shape priors using orientation information to model white matter tracts from magnetic resonance diffusion tensor images (dti). shape normalization is needed for the construction of a shape prior using statistical methods. moving beyond shape normalization using boundary-only or orientation-only information, our method combines the idea of sweeping and inverse-skeletonization to parameterize 3d volumetric shape, which provides point correspondence and orientations over the whole volume in a continuous fashion. tangents from this continuous model can be treated as a de-noised reconstruction of the original structural orientation inside a shape. we demonstrate the accuracy of this technique by reconstructing synthetic data and the 3d cingulum tract from brain dti data and manually drawn 2d contours for each tract. our output can also serve as the input for subsequent boundary finding or shape analysis.
multiple q-shell odf reconstruction in q-ball imaging. q-ball imaging (qbi) is a high angular resolution diffusion imaging (hardi) technique which has been proven very successful in resolving multiple intravoxel fiber orientations in mr images. the standard computation of the orientation distribution function (odf, the probability of diffusion in a given direction) from q-ball uses linear radial projection, neglecting the change in the volume element along the ray, thereby resulting in distributions different from the true odfs. a new technique has been recently proposed that, by considering the solid angle factor, uses the mathematically correct definition of the odf and results in a dimensionless and normalized odf expression from a single q-shell. in this paper, we extend this technique in order to exploit hardi data from multiple q-shells. we consider the more flexible multi-exponential model for the diffusion signal, and show how to efficiently compute the odfs in constant solid angle. we describe our method and demonstrate its improved performance on both artificial and real hardi data.
tissue tracking in thermo-physiological imagery through spatio-temporal smoothing. accurate tracking of facial tissue in thermal infrared imaging is challenging because it is affected not only by positional but also physiological (functional) changes. this article presents a particle filter tracker driven by a probabilistic template function with both spatial and temporal smoothing components, which is capable of adapting to abrupt positional and physiological changes. the method was tested on tracking facial regions of subjects under varying physiological and environmental conditions in 12 thermal clips. it demonstrated robustness and accuracy, outperforming other strategies. this new method promises improved performance in a host of biomedical applications that involve physiological measurements on the face, like unobtrusive sleep studies.
thermal vision for sleep apnea monitoring. the present paper proposes a novel methodology to monitor sleep apnea through thermal imaging. first, the nostril region is segmented and it is tracked over time via a network of cooperating probabilistic trackers. then, the mean thermal signal of the nostril region, carrying the breathing information, is analyzed through wavelet decomposition. the experimental set included 22 subjects (12 men and 10 women). the sleep-disordered incidents were detected by both thermal and standard polysomnographic methodologies. the high accuracy confirms the validity of the proposed approach, and brings non-obtrusive clinical monitoring of sleep disorders within reach.
modeling respiratory motion for cancer radiation therapy based on patient-specific 4dct data. prediction of respiratory motion has the potential to substantially improve cancer radiation therapy. a nonlinear finite element (fe) model of respiratory motion during full breathing cycle has been developed based on patient specific pressure-volume relationship and 4d computed tomography (ct) data. for geometric modeling of lungs and ribcage we have constructed intermediate cad surface which avoids multiple geometric smoothing procedures. for physiologically relevant respiratory motion modeling we have used pressure-volume (pv) relationship to apply pressure loading on the surface of the model. a hyperelastic soft tissue model, developed from experimental observations, has been used. additionally, pleural sliding has been considered which results in accurate deformations in the superior-inferior (si) direction. the finite element model has been validated using 51 landmarks from the ct data. the average differences in position is seen to be 0.07 cm (sd = 0.20 cm), 0.07 cm (0.15 cm), and 0.22 cm (0.18 cm) in the left-right, anterior-posterior, and superior-inferior directions, respectively.
extending genetic linkage analysis to diffusion tensor images to map single gene effects on brain fiber architecture. we extended genetic linkage analysis - an analysis widely used in quantitative genetics - to 3d images to analyze single gene effects on brain fiber architecture. we collected 4 tesla diffusion tensor images (dti) and genotype data from 258 healthy adult twins and their non-twin siblings. after high-dimensional fluid registration, at each voxel we estimated the genetic linkage between the single nucleotide polymorphism (snp), val66met (dbsnp number rs6265), of the bdnf gene (brain-derived neurotrophic factor) with fractional anisotropy (fa) derived from each subject's dti scan, by fitting structural equation models (sem) from quantitative genetics. we also examined how image filtering affects the effect sizes for genetic linkage by examining how the overall significance of voxelwise effects varied with respect to full width at half maximum (fwhm) of the gaussian smoothing applied to the fa images. raw fa maps with no smoothing yielded the greatest sensitivity to detect gene effects, when corrected for multiple comparisons using the false discovery rate (fdr) procedure. the bdnf polymorphism significantly contributed to the variation in fa in the posterior cingulate gyrus, where it accounted for around 90-95% of the total variance in fa. our study generated the first maps to visualize the effect of the bdnf gene on brain fiber integrity, suggesting that common genetic variants may strongly determine white matter integrity.
3d prostate segmentation in ultrasound images based on tapered and deformed ellipsoids. prostate segmentation from trans-rectal transverse b-mode ultrasound images is required for radiation treatment of prostate cancer. manual segmentation is a time-consuming task, the results of which are dependent on image quality and physicians' experience. this paper introduces a semi-automatic 3d method based on super-ellipsoidal shapes. it produces a 3d segmentation in less than 15 seconds using a warped, tapered ellipsoid fit to the prostate. a study of patient images shows good performance and repeatability. this method is currently in clinical use at the vancouver cancer center where it has become the standard segmentation procedure for low dose-rate brachytherapy treatment.
automatic image-based cardiac and respiratory cycle synchronization and gating of image sequences. we propose a novel method to detect the current state of the quasi-periodic system from image sequences which in turn will enable us to synchronize/gate the image sequences to obtain images of the organ system at similar configurations. the method uses the cumulated phase shift in the spectral domain of successive image frames as a measure of the net motion of objects in the scene. the proposed method is applicable to 2d and 3d time varying sequences and is not specific to the imaging modality. we demonstrate its effectiveness on x-ray angiographic and cardiac and liver ultrasound sequences. knowledge of the current (cardiac or respiratory) phase of the system, opens up the possibility for a purely image based cardiac and respiratory gating scheme for interventional and radiotherapy procedures.
predicting mgmt methylation status of glioblastomas from mri texture. in glioblastoma (gbm), promoter methylation of the dna repair gene mgmt is associated with benefit from chemotherapy. because mgmt promoter methylation status can not be determined in all cases, a surrogate for the methylation status would be a useful clinical tool. correlation between methylation status and magnetic resonance imaging features has been reported suggesting that non-invasive mgmt promoter methylation status detection is possible. in this work, a retrospective analysis of t2, flair and t1-post contrast mr images in patients with newly diagnosed gbm is performed using l1-regularized neural networks. tumor texture, assessed quantitatively was utilized for predicting the mgmt promoter methylation status of a gbm in 59 patients. the texture features were extracted using a space-frequency texture analysis based on the s-transform and utilized by a neural network to predict the methylation status of a gbm. blinded classification of mgmt promoter methylation status reached an average accuracy of 87.7%, indicating that the proposed technique is accurate enough for clinical use.
left ventricle segmentation using diffusion wavelets and boosting. we propose a method for the segmentation of medical images based on a novel parameterization of prior shape knowledge and a search scheme based on classifying local appearance. the method uses diffusion wavelets to capture arbitrary and continuous interdependencies in the training data and uses them for an efficient shape model. the lack of classic visual consistency in complex medical imaging data, is tackled by a manifold learning approach handling optimal high-dimensional local features by gentle boosting. appearance saliency is encoded in the model and segmentation is performed through the extraction and classification of the corresponding features in a new data set, as well as a diffusion wavelet based shape model constraint. our framework supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. promising results are reported for heart ct data sets, proving the impact of the soft parameterization, and the efficiency of our approach.