waashk/dblp huggingface dataset:
evaluation of a novel calibration technique for optically tracked oblique laparoscopes. this paper proposes an evaluation of a novel calibration method for an optically tracked oblique laparoscope. we present the necessary tools to track an oblique scope and a camera model which includes changes to the intrinsic camera parameters thereby extending previously proposed methods. because oblique scopes offer a wide 'virtual' view on the surgical field, the method is of great interest for augmented reality guidance of laparoscopic interventions using an oblique scope. the model and an approximated version are evaluated in an extensive validation study. using 5 sets of 40 calibration images, we compare both camera models (i.e. model and approximation) and 2 interpolation schemes. the selected model and interpolation scheme reaches an average accuracy of 2.60 pixel and an equivalent 3d error of 0.60 mm. finally, we present initial experience of the presented approach with an oblique scope and optical tracking in a clinical setup. during a laparoscopic rectum resection surgery the setup was used to augment the scene with a model of the pelvis. the method worked properly and the attached probes did not interfere with normal procedure.
automated planning of scan geometries in spine mri scans. consistency of mr scan planning is very important for diagnosis, especially in multi-site trials and follow-up studies, where disease progress or response to treatment is evaluated. accurate manual scan planning is tedious and requires skillful operators. on the other hand, automated scan planning is difficult due to relatively low quality of survey images ("scouts") and strict processing time constraints. this paper presents a novel method for automated planning of mri scans of the spine. lumbar and cervical examinations are considered, although the proposed method is extendible to other types of spine examinations, such as thoracic or total spine imaging. the automated scan planning (asp) system consists of an anatomy recognition part, which is able to automatically detect and label the spine anatomy in the scout scan, and a planning part, which performs scan geometry planning based on recognized anatomical landmarks. a validation study demonstrates the robustness of the proposed method and its feasibility for clinical use.
a bayesian approach for liver analysis: algorithm and validation study. we present a new method for the simultaneous, nearly automatic segmentation of liver contours, vessels, and metastatic lesions from abdominal cta scans. the method repeatedly applies multi-resolution, multi-class smoothed bayesian classification followed by morphological adjustment and active contours refinement. it uses multi-class and voxel neighborhood information to compute an accurate intensity distribution function for each class. the method requires only one or two user-defined voxel seeds, with no manual adjustment of internal parameters. a retrospective study on two validated clinical datasets totaling 56 ctas was performed. we obtained correlations of 0.98 and 0.99 with a manual ground truth liver volume estimation for the first and second databases, and a total score of 67.87 for the second database. these results suggest that our method is accurate, efficient, and robust to seed selection compared to manually generated ground truth segmentation and to other semi-automatic segmentation methods.
a comparison of methods for recovering intra-voxel white matter fiber architecture from clinical diffusion imaging scans. diffusion tensor magnetic resonance imaging is widely used to study the structure of the fiber pathways of brain white matter. however, the diffusion tensor cannot capture complex intra-voxel fiber architecture such as fiber crossings. consequently, a number of methods have been proposed to recover intra-voxel fiber bundle orientations from high angular-resolution diffusion imaging scans, which are optimized to resolve fiber crossings. in this work we study how multi-tensor, spherical deconvolution, analytical qball and diffusion basis function methods perform under clinical scanning conditions. our experiments indicate that it is feasible to apply some of these methods in clinical data sets.
mean q-ball strings obtained by constrained procrustes analysis with point sliding. the idea underpinning the work we present herein is to design robust and objective tools for brain white matter (wm) morphometry. we focus on wm tracts, and propose to represent them by their mean lines, to which we associate the attributes derived from high-angular resolution diffusion imaging (hardi). the definition of the tract mean line derives directly from the geometry of the tract fibres. we determine the fibre point correspondences and impact factors of individual fibres, upon which we estimate average hardi models along the tract mean lines. this way we obtain a compact tract representation that exploits all the available information, and is at the same time free of the outlier influence and undesired tract edge effects.
spectral clustering as a diagnostic tool in cross-sectional mr studies: an application to mild dementia. structural imaging investigations commonly apply a segmentation step followed by the extraction of feature data that can be used to compare or discriminate groups. we present a framework for such a study based on automated multi-atlas segmentation followed by the extraction of low-level morphological features, volumes and overlaps, for classification. a spectral analysis step is used to transform pairwise overlap information into feature data that relate to individual subjects. applying the framework to a group of controls and patients with mild dementia, we compare the volume- and overlap-based classification performance using both supervised and unsupervised classifiers. the results indicate that unsupervised classification following a spectral analysis of label overlaps performs very well, outperforming classifiers that use volumes alone.
computational atlases of severity of white matter lesions in elderly subjects with mri. mri of cerebral white matter may show regions of signal abnormalities. these changes may be associated with hypertension, inflammation, or ischemia, as well as altered brain function. the goal of this work has been to construct computational atlases of white matter lesions that represent both their severity as well as the frequency of their occurrence in a population to achieve a better classification of white matter disease. an atlas is computed with a pipeline that uses 4t flair and 4t t1-weighted (t1w) brain images of a group of subjects. the processing steps include intensity correction, lesion extraction, intra-subject flair to t1w rigid registration, and seamless replacement of lesions in t1w images with synthetic white matter texture. subsequently, the t1w images and lesion images of different subjects are registered non-rigidly to the same space. the decrease in t1w intensities is used to obtain severity information. atlases were constructed for two groups of subjects, elderly normal controls or with mild cognitive impairment, and subjects with cerebrovascular disease. the lesion severities of the two groups have a significant statistical difference with the severity in the atlas of cerebrovascular disease being higher.
cardiac electrophysiology model adjustment using the fusion of mr and optical imaging. despite important recent efforts in cardiac electrophysiology modelling, there is still a strong need for validating macroscopic models, that are well suited for diagnosis and treatment planning. in this paper we present a method to adjust the parameters of a macroscopic electrophysiology model on depolarisation and repolarisation maps obtained ex-vivo from optical imaging. with this imaging technique, optical fluorescence data are recorded with high spatial and temporal resolution on a large healthy porcine heart. a model of the myocardium is built from the mr images of the same heart, which also integrates the myocardial fibre orientation measured with dti. we then present the first quantitative adjustment of a personalised volumetric model of the myocardium.
surface-based vector analysis using heat equation interpolation: a new approach to quantify local hippocampal volume changes. analysis of surface-based displacement vectors using spherical harmonic description (spharm) localizes shape changes accurately. however, it does not allow differentiating volume variations from shifting and/or bending. we propose a new approach to quantify local volume changes by computing the surface-based jacobian determinant. this measurement is computed on the displacement vector fields estimated by a heat equation interpolation on the displacement vectors produced by spharm. data simulation showed that the surface-based jacobian determinant enables accurate quantification of local volume changes without interference of shifting/bending. in patients with temporal lobe epilepsy and left hippocampal atrophy, spharm detected widespread inward deformation related to atrophy in the hippocampal head and body, and showed areas of mirrored inward/outward deformations mostly at the level of the hippocampal tail. in these areas, the surface-based jacobian determinant showed atrophy. our method facilitates the interpretation of spharm because it allows decomposing volume changes and shifting/bending. furthermore, it provides a better delineation of the extent of hippocampal atrophy.
robust segmentation and anatomical labeling of the airway tree from thoracic ct scans. a method for automatic extraction and labeling of the airway tree from thoracic ct scans is presented and extensively evaluated on 150 scans of clinical dose, low dose and ultra-low dose data, in inspiration and expiration from both relatively healthy and severely ill patients. the method uses adaptive thresholds while growing the airways and it is shown that this strategy leads to a substantial increase in the number, total length and number of correctly labeled airways extracted. from inspiration scans on average 170 branches are found, from expiration scans 59.
dynamic probabilistic atlas of functional brain regions for transcranial magnetic stimulation. transcranial magnetic stimulation (tms) is a technique to stimulate the brain non-invasively. the applications range from accurate localization of the primary motor areas to potential treatment of disorders such as tinnitus, severe depression, and pain. stereotactic guidance requires individual mr images of the subject's head, which is in some applications typically omitted due to financial motivations. in this paper, we introduce a method that offers improved tms pulse targeting also to those subjects who do not have mr examinations. a probabilistic brain model was constructed by spatially normalizing the locations of the functional brain areas in a study population, and modeling the distributions and estimates of the locations of the functional brain regions using probabilistic methods. the application of the probabilistic brain model to the target subject was based on a point set determined from the scalp and facial skin of the target subject. the methods were evaluated using data from four functional brain areas from 56 healthy subjects. the accuracy of the estimates of the locations of the functional brain regions was about nine millimeters.
a novel method for cortical sulcal fundi extraction. sulcal fundi are 3d curves along the bottom of sulcal regions of the human cerebral cortex. in this paper, we propose a novel automatic method for extraction of sulcal fundi from triangulated cortical surface. compared to existing methods, the proposed method can find accurate sulcal fundi using curvatures and curvature derivatives without manual interaction. given a triangulated cortical surface, our method is composed of four steps: estimating curvatures and curvature derivatives for each vertex, detecting the sulcal fundi segments in each triangle, linking the sulcal fundi segments and combining of adjacent sulcal fundi, and connecting breaking sulcal fundi and smoothing using the fast marching method on the cortical surface. the proposed sulcal fundi extraction method is applied to ten normal brain inner cortical surfaces. we quantitatively validated the proposed method of sulcal fundi extraction using manually labeled sulcal fundi by experts as the ground truth.
mri bone segmentation using deformable models and shape priors. this paper addresses the problem of automatically segmenting bone structures in low resolution clinical mri datasets. the novel aspect of the proposed method is the combination of physically-based deformable models with shape priors. models evolve under the influence of forces that exploit image information and prior knowledge on shape variations. the prior defines a principal component analysis (pca) of global shape variations and a markov random field (mrf) of local deformations, imposing spatial restrictions in shapes evolution. for a better efficiency, various levels of details are considered and the differential equations system is solved by a fast implicit integration scheme. the result is an automatic multilevel segmentation procedure effective with low resolution images. experiments on femur and hip bones segmentation from clinical mri depict a promising approach (mean accuracy: 1.44±1.1 mm, computation time: 2mn43s).
fast marker based c-arm pose estimation. to estimate the pose of a c-arm during interventions therapy we have developed a small sized x-ray target including a special set of beads with known locations in 3d space. since the patient needs to remain in the x-ray path for all feasible poses of the c-arm during the intervention, we cannot construct a single marker which is entirely visible in all images. therefore finding 2d-3d point correspondences is a non-trivial task. the marker pattern has to be chosen in a way such that its projection onto the image plane is unique in a minimal-sized window for all relevant poses of the c-arm. we use a two dimensional adaption of a linear feedback shift register (lfsr) to generate a two-dimensional pattern with unique sub-patterns in a certain window range. thereby uniqueness is not achieved by placing unique 2d sub patterns side by side but by the code property itself. the code is designed in a way that any sub window of a minimal size guarantees uniqueness and that even occlusions from medical instruments can be handled. experiments showed that we were able to estimate the c-arm's pose from a single image within one second with a precision below one millimeter and one degree.
evaluation of a cardiac ultrasound segmentation algorithm using a phantom. this paper evaluates the performance of a level set algorithm for segmenting the endocardium in short-axis ultrasound images. the evaluation is carried out using an anthropomorphic ultrasound phantom. details of the phantom design, including comparison of the ultrasound parameters with in-vitro measurements, are included.in addition to measuring segmentation accuracy, the effectiveness of the energy minimization scheme is also determined. it is argued that using the phantom along with global minimization algorithms (simulated annealing and random search) makes is possible to assess the minimization strategy.
a novel explicit 2d+t cyclic shape model applied to echocardiography. in this paper, we propose a novel explicit 2d+t cyclic shape model that extends the point distribution model (pdm) to shapes like myocardial contours with cyclic dynamics. we also propose an extension to procrustes alignment that removes pose and subject size variability while maintaining dynamic effects. our model draws on ideas from principal component analysis (pca), multidimensional scaling (mds) and kernel pca (kpca) and solves 3 shortcomings of previous implicit models: 1) cardiac cycles in the data set do not each need to have the same number of frames, 2) the required number of subjects for statistically significant results is substantially reduced and 3) the displacement of contour points incorporates time as an explicit variable. we illustrate our method by computing models of the myocardium in the 4 principal planes of 2d+t echocardiography data.
identifying regional cardiac abnormalities from myocardial strains using spatio-temporal tensor analysis. myocardial deformation is a critical indicator of many cardiac diseases and dysfunctions. the goal of this paper is to use myocardial deformation patterns to identify and localize regional abnormal cardiac function in human subjects. we have developed a novel tensor-based classification framework that better conserves the spatio-temporal structure of the myocardial deformation pattern than conventional vector-based algorithms. in addition, the tensor-based projection function keeps more of the information of the original feature space, so that abnormal tensors in the subspace can be back-projected to reveal the regional cardiac abnormality in a more physically meaningful way. we have tested our novel method on 41 human image sequences, and achieved a classification rate of 87.80%. the recovered regional abnormalities from our algorithm agree well with the patient's pathology and doctor's diagnosis and provide a promising avenue for regional cardiac function analysis.
joint segmentation of thalamic nuclei from a population of diffusion tensor mr images. several recent studies explored the use of unsupervised segmentation methods for segmenting thalamic nuclei from diffusion tensor images. these methods provide a plausible segmentation on individual subjects; however, they do not address the problem of consistently identifying the same functional areas in a population. the lack of correspondence between the segmented nuclei make it more difficult to use the results from the unsupervised segmentation tools for morphometry. in this paper we present a novel segmentation algorithm to automatically segment the gray matter nuclei while ensuring consistency between subjects in a population. this new algorithm, referred to as consistency clustering, finds correspondence between the nuclei as the segmentation is achieved through a single model for the whole population, similar to the brain atlases experts use to identify thalamic nuclei.
unbiased stratification of left ventricles. image based quantitative stratification of the left ventricles (lv) across a population helps in unraveling the structure-function symbiosis of the heart. an unbiased, reference less grouping scheme that automatically determines the number of clusters and a physioanatomically relevant strategy that aligns the intra cluster lv shapes would enable the robust construction of pathology stratified cardiac atlas. this paper achieves this hitherto elusive stratification and alignment by adapting the conventional strategies routinely followed by clinicians. the individual lv shape models (n=127) are independently oriented to an "attitudinally consistent orientation" that captures the physioanatomic variations of the lv morphology. affinity propagation technique based on the automatically identified inter-lv_landmark distances is used to group the lv shapes. the proposed algorithm is computationally efficient and, if the inter cluster variations are linked to pathology, could provide a clinically relevant cardiac atlas.
multivariate statistical analysis of whole brain structural networks obtained using probabilistic tractography. this paper presents a new framework for the analysis of anatomical connectivity derived from diffusion tensor mri. the framework has been applied to estimate whole brain structural networks using diffusion data from 174 adult subjects. in the proposed approach, each brain is first segmented into 83 anatomical regions via label propagation of multiple atlases and subsequent decision fusion. for each pair of anatomical regions the probability of connection and its strength is then estimated using a modified version of probabilistic tractography. the resulting brain networks have been classified according to age and gender using non-linear support vector machines with gentleboost feature extraction. classification performance was tested using a leave-one-out approach and the mean accuracy obtained was 85.4%.
lumbar disc localization and labeling with a probabilistic model on both pixel and object features. repeatable, quantitative assessment of intervertebral disc pathology requires accurate localization and labeling of the lumbar region discs. to that end, we propose a two-level probabilistic model for such disc localization and labeling. our model integrates both pixel-level information, such as appearance, and object-level information, such as relative location. utilizing both levels of information adds robustness to the ambiguous disc intensity signature and high structure variation. yet, we are able to do efficient (and convergent) localization and labeling with generalized expectation-maximization. we present accurate results on 20 normal cases (96%) and a promising extension to a pathology case.
semi-supervised nasopharyngeal carcinoma lesion extraction from magnetic resonance images using online spectral clustering with a learned metric. in this paper, we consider the extraction of nasopharyngeal carcinoma lesion from mr images as a region segmentation problem. we propose a semi-supervised segmentation approach to segment the lesion in two steps. first, a metric is learned in a supervised fashion, which maximizes the separation between two groups of pixels (tumor or non-tumor) with minimal user interaction. second, the learned metric is used to complete extraction of tumor region in an unsupervised fashion. several experiments were conducted to evaluate the performance of similar methods with learned metrics for grouping or classifying pixels to form the tumor region. it is observed that the spectral clustering-based method performs well and the performance is comparable or marginally better than the discriminative svm-based method.
prediction of biomechanical parameters of the proximal femur using statistical appearance models and support vector regression. fractures of the proximal femur are one of the principal causes of mortality among elderly persons. traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. however, bmd alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose. in this work an approach that uses statistical models of appearance to identify relevant regions and parameters for the prediction of biomechanical properties of the proximal femur will be presented. by using support vector regression the proposed model based approach is capable of predicting two different biomechanical parameters accurately and fully automatically in two different testing scenarios.