waashk/dblp huggingface dataset:
using frankenstein's creature paradigm to build a patient specific atlas. conformal radiotherapy planning needs accurate delineations of the critical structures. atlas-based segmentation has been shown to be very efficient to delineate brain structures. it would therefore be very interesting to develop an atlas for the head and neck region where 7 % of the cancers arise. however, the construction of an atlas in this region is very difficult due to the high variability of the anatomies. this can generate segmentation errors and over-segmented structures in the atlas. to overcome this drawback, we present an alternative method to build a template locally adapted to the patient's anatomy. this is done first by selecting in a database the images that are the most similar to the patient on predefined regions of interest, using on a distance between transformations. the first major contribution is that we do not compute every patient-to-image registration to find the most similar image, but only the registration of the patient towards an average image. this method is therefore computationally very efficient. the second major contribution is a novel method to use the selected images and the predefined regions to build a "frankenstein's creature" for segmentation. 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 58 patients among a database of 105 head and neck ct images and shows a great improvement of the specificity of the results.
on the blurring of the funk-radon transform in q-ball imaging. one known issue in q---ball imaging is the blurring in the radial integral defining the orientation distribution function of fiber bundles, due to the computation of the funk---radon transform (frt). three novel techniques to overcome this problem are presented, all of them based upon different assumptions about the behavior of the attenuation signal outside the sphere densely sampled from hardi data sets. a systematic study with synthetic data has been carried out to show that the frt blurring is not as important as the error introduced by some unrealistic assumptions, and only one of the three techniques (the one with the less restrictive assumption) improves the accuracy of q---balls.
a new approach for creating customizable cytoarchitectonic probabilistic maps without a template. we present a novel technique for creating template-free probabilistic maps of the cytoarchitectonic areas using a groupwise registration. we use the technique to transform 10 human post-mortem structural mr data sets, together with their corresponding cytoarchitectonic information, to a common space. we have targeted the cytoarchitectonically defined subregions of the primary auditory cortex. thanks to the template-free groupwise registration, the created maps are not macroanatomically biased towards a specific geometry/topology. the advantage of the groupwise versus pairwise registration in avoiding such anatomical bias is better revealed in studies with small number of subjects and a high degree of variability among the individuals such as the post-mortem data. a leave-one-out cross-validation method was used to compare the sensitivity, specificity and positive predictive value of the proposed and published maps. we observe a significant improvement in localization of cytoarchitectonically defined subregions in primary auditory cortex using the proposed maps. the proposed maps can be tailored to any subject space by registering the subject image to the average of the groupwise-registered post-mortem images.
intra-retinal layer segmentation in optical coherence tomography using an active contour approach. optical coherence tomography (oct) is a non-invasive, depth resolved imaging modality that has become a prominent ophthalmic diagnostic technique. we present an automatic segmentation algorithm to detect intra-retinal layers in oct images acquired from rodent models of retinal degeneration. we adapt chan---vese's energy-minimizing active contours without edges for oct images, which suffer from low contrast and are highly corrupted by noise. we adopt a multi-phase framework with a circular shape prior in order to model the boundaries of retinal layers and estimate the shape parameters using least squares. we use a contextual scheme to balance the weight of different terms in the energy functional. the results from various synthetic experiments and segmentation results on 20 oct images from four rats are presented, demonstrating the strength of our method to detect the desired retinal layers with sufficient accuracy and average dice similarity coefficient of 0.85, specifically 0.94 for the the ganglion cell layer, which is the relevant layer for glaucoma diagnosis.
genetics of anisotropy asymmetry: registration and sample size effects. brain asymmetry has been a topic of interest for neuroscientists for many years. the advent of diffusion tensor imaging (dti) allows researchers to extend the study of asymmetry to a microscopic scale by examining fiber integrity differences across hemispheres rather than the macroscopic differences in shape or structure volumes. even so, the power to detect these microarchitectural differences depends on the sample size and how the brain images are registered and how many subjects are studied. we fluidly registered 4 tesla dti scans from 180 healthy adult twins (45 identical and fraternal pairs) to a geometrically-centered population mean template. we computed voxelwise maps of significant asymmetries (left/right hemisphere differences) for common fiber anisotropy indices (fa, ga). quantitative genetic models revealed that 47-62% of the variance in asymmetry was due to genetic differences in the population. we studied how these heritability estimates varied with the type of registration target (t1- or t2-weighted) and with sample size. all methods consistently found that genetic factors strongly determined the lateralization of fiber anisotropy, facilitating the quest for specific genes that might influence brain asymmetry and fiber integrity.
biomechanically-constrained 4d estimation of myocardial motion. we propose a method for the analysis of cardiac images with the goal of reconstructing the motion of the ventricular walls. the main feature of our method is that the inversion parameter field is the active contraction of the myocardial fibers. this is accomplished with a biophysically-constrained, four-dimensional (space plus time) formulation that aims to complement information that can be gathered from the images by a priori knowledge of cardiac mechanics. our main hypothesis is that by incorporating biophysical information, we can generate more informative priors and thus, more accurate predictions of the ventricular wall motion. in this paper, we outline the formulation, discuss the computational methodology for solving the inverse motion estimation, and present preliminary validation using synthetic and tagged mr images. the overall method uses patient-specific imaging and fiber information to reconstruct the motion. in these preliminary tests, we verify the implementation and conduct a parametric study to test the sensitivity of the model to material properties perturbations, model errors, and incomplete and noisy observations.
enforcing monotonic temporal evolution in dry eye images. we address the problem of identifying dry areas in the tear film as part of a diagnostic tool for dry-eye syndrome. the requirement is to identify and measure the growth of the dry regions to provide a time-evolving map of degrees of dryness. we segment dry regions using a multi-label graph-cut algorithm on the 3d spatio-temporal volume of frames from a video sequence. to capture the fact that dryness increases over the time of the sequence, we use a time-asymmetric cost function that enforces a constraint that the dryness of each pixel monotonically increases. we demonstrate how this increases our estimation's reliability and robustness. we tested the method on a set of videos and suggest further research using a similar approach.
improved modelling of ultrasound contrast agent diminution for blood perfusion analysis. ultrasound contrast imaging is increasingly used to analyze blood perfusion in cases of ischemic or cancerous diseases. among other imaging methods, the diminution harmonic imaging (dhi), which modells the diminution of contrast agent due to ultrasound pulses, is the most promising because of its speed. however, the current imaging quality of dhi is insufficient for reliable diagnoses.in this paper, we extend the mathematical dhi model to include the part of the intensity signal which is due to tissue reflections and other effects not based on the contrast agent and its concentration in the blood. we show in a phantom experiment with available perfusion ground truth the vast improvements in accuracy of the new model. our findings also strongly support the theory of a linear relationship between the perfusion speed and the determined perfusion coefficient, which is a large step towards quantitative perfusion measurements.
tensor-based morphometry with mappings parameterized by stationary velocity fields in alzheimer's disease neuroimaging initiative. tensor-based morphometry (tbm) is an analysis technique where anatomical information is characterized by means of the spatial transformations between a customized template and observed images. therefore, accurate inter-subject non-rigid registration is an essential prerrequisite. further statistical analysis of the spatial transformations is used to highlight some useful information, such as local statistical differences among populations. with the new advent of recent and powerful non-rigid registration algorithms based on the large deformation paradigm, tbm is being increasingly used. in this work we evaluate the statistical power of tbm using stationary velocity field diffeomorphic registration in a large population of subjects from alzheimer's disease neuroimaging initiative project. the proposed methodology provided atrophy maps with very detailed anatomical resolution and with a high significance compared with results published recently on the same data set.
a deformation tracking approach to 4d coronary artery tree reconstruction. this paper addresses reconstruction of a temporally deforming 3d coronary vessel tree, i.e., 4d reconstruction from a sequence of angiographic x-ray images acquired by a rotating c-arm. our algorithm starts from a 3d coronary tree that was reconstructed from images of one cardiac phase. driven by gradient vector flow (gvf) fields, the method then estimates deformation such that projections of deformed models align with x-ray images of corresponding cardiac phases. to allow robust tracking of the coronary tree, the deformation estimation is regularized by smoothness and cyclic deformation constraints. extensive qualitative and quantitative tests on clinical data sets suggest that our algorithm reconstructs accurate 4d coronary trees and regularized estimation significantly improves robustness. our experiments also suggest that a hierarchy of deformation models with increasing complexities are desirable when input data are noisy or when the quality of the 3d model is low.
contact studies between total knee replacement components developed using explicit finite elements analysis. a pneumatic simulator of the knee joint with five dof was developed to determine the correlation between the kinematics of the knee joint, and the wear of the polyethylene componenent of a tkr prosthesis. a physical model of the knee joint with total knee replacement (tkr) was built by rapid-prototyping based on ct images from a patient. a clinically-available prosthesis was mounted on the knee model. using a video analysis system, and two force and contact pressure plates, the kinematics and kinetics data were recorded during normal walking of the patient. the quadriceps muscle force during movement was computed using the anybody software. joint loadings were generated by the simulator based on recorded and computed data. using the video analysis system, the precise kinematics of the artificial joint from the simulator was recorded and used as input for an explicit dynamics fe analysis of the joint. the distribution of the contact stresses in the implant was computed during the walking cycle to analyze the prosthesis behavior. the results suggest that the combination of axial loading and anterior-posterior stress is responsible for the abrasive wear of the polyethylene component of the prosthesis.
atlas-based automated segmentation of spleen and liver using adaptive enhancement estimation. the paper presents the automated segmentation of spleen and liver from contrast-enhanced ct images of normal and hepato/splenomegaly populations. the method used 4 steps: (i) a mean organ model was registered to the patient ct; (ii) the first estimates of the organs were improved by a geodesic active contour; (iii) the contrast enhancements of liver and spleen were estimated to adjust to patient image characteristics, and an adaptive convolution refined the segmentations; (iv) lastly, a normalized probabilistic atlas corrected for shape and location for the precise computation of each organ's volume and height (mid-hepatic liver height and cephalocaudal spleen height). results from test data demonstrated the method's ability to accurately segment the spleen (rms error = 1.09mm; dice/tanimoto overlaps = 95.2/91) and liver (rms error = 2.3mm, and dice/tanimoto overlaps = 96.2/92.7). the correlations (r2) with clinical/manual height measurements were 0.97 and 0.93 for the spleen and liver respectively.
pattern recognition of abnormal left ventricle wall motion in cardiac mr. there are four main problems that limit application of pattern recognition techniques for recognition of abnormal cardiac left ventricle (lv) wall motion: 1) normalization of the lv's size, shape, intensity level and position; 2) defining a spatial correspondence between phases and subjects; 3) extracting features; 4) and discriminating abnormal from normal wall motion. solving these four problems is required for application of pattern recognition techniques to classify the normal and abnormal lv wall motion. in this work, we introduce a normalization scheme to solve the first and second problems. with this scheme, lvs are normalized to the same position, size, and intensity level. using the normalized images, we proposed an intra-segment classification criterion based on a correlation measure to solve the third and fourth problems. application of the method to recognition of abnormal cardiac mr lv wall motion showed promising results.
segmentation and classification of cell cycle phases in fluorescence imaging. current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. stable cell lines expressing fluorescently tagged protein gfp-pcna produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the s-phase. variable fluorescence patterns, drastic changes in snr, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. we extend the recently proposed graph partitioning active contours (gpac) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. we utilize surface shape properties of gfp-pcna intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.
learning copd sensitive filters in pulmonary ct. the standard approaches to analyzing emphysema in computed tomography (ct) images are visual inspection and the relative area of voxels below a threshold (ra). the former approach is subjective and impractical in a large data set and the latter relies on a single threshold and independent voxel information, ignoring any spatial correlation in intensities. in recent years, supervised learning on texture features has been investigated as an alternative to these approaches, showing good results. however, supervised learning requires labeled samples, and these samples are often obtained via subjective and time consuming visual scoring done by human experts.in this work, we investigate the possibility of applying supervised learning using texture measures on random ct samples where the labels are based on external, non-ct measures. we are not targeting emphysema directly, instead we focus on learning textural differences that discriminate subjects with chronic obstructive pulmonary disease (copd) from healthy smokers, and it is expected that emphysema plays a major part in this. the proposed texture based approach achieves an 69% classification accuracy which is significantly better than ra's 55% accuracy.
building shape models from lousy data. statistical shape models have gained widespread use in medical image analysis. in order for such models to be statistically meaningful, a large number of data sets have to be included. the number of available data sets is usually limited and often the data is corrupted by imaging artifacts or missing information. we propose a method for building a statistical shape model from such "lousy" data sets. the method works by identifying the corrupted parts of a shape as statistical outliers and excluding these parts from the model. only the parts of a shape that were identified as outliers are discarded, while all the intact parts are included in the model. the model building is then performed using the em algorithm for probabilistic principal component analysis, which allows for a principled way to handle missing data. our experiments on 2d synthetic and real 3d medical data sets confirm the feasibility of the approach. we show that it yields superior models compared to approaches using robust statistics, which only downweight the influence of outliers.
segmentation of lumbar vertebrae using part-based graphs and active appearance models. the aim of the work is to provide a fully automatic method of segmenting vertebrae in spinal radiographs. this is of clinical relevance to the diagnosis of osteoporosis by vertebral fracture assessment, and to grading incident fractures in clinical trials. we use a parts based model of small vertebral patches (e.g. corners). many potential candidates are found in a global search using multi-resolution normalised correlation. the ambiguity in the possible solution is resolved by applying a graphical model of the connections between parts, and applying geometric constraints. the resulting graph optimisation problem is solved using loopy belief propagation.the minimum cost solution is used to initialise a second phase of active appearance model search. the method is applied to a clinical data set of computed radiography images of lumbar spines. the accuracy of this fully automatic method is assessed by comparing the results to a gold standard of manual annotation by expert radiologists.
predictive simulation of bidirectional glenn shunt using a hybrid blood vessel model. this paper proposes a method for performing predictive simulation of cardiac surgery. it applies a hybrid approach to model the deformation of blood vessels. the hybrid blood vessel model consists of a reference cosserat rod and a surface mesh. the reference cosserat rod models the blood vessel's global bending, stretching, twisting and shearing in a physically correct manner, and the surface mesh models the surface details of the blood vessel. in this way, the deformation of blood vessels can be computed efficiently and accurately. our predictive simulation system can produce complex surgical results given a small amount of user inputs. it allows the surgeon to easily explore various surgical options and evaluate them. tests of the system using bidirectional glenn shunt (bdg) as an application example show that the results produced by the system are similar to real surgical results.
robust atlas-based brain segmentation using multi-structure confidence-weighted registration. we present a robust and accurate atlas-based brain segmentation method which uses multiple initial structure segmentations to simultaneously drive the image registration and achieve anatomically constrained correspondence. we also derive segmentation confidence maps (scms) from a given manually segmented training set; these characterize the accuracy of a given set of segmentations as compared to manual segmentations. we incorporate these in our cost term to weight the influence of initial segmentations in the multi-structure registration, such that low confidence regions are given lower weight in the registration. to account for correspondence errors in the underlying registration, we use a supervised atlas correction technique and present a method for correcting the atlas segmentation to account for possible errors in the underlying registration. we applied our multi-structure atlas-based segmentation and supervised atlas correction to segment the amygdala in a set of 23 autistic patients and controls using leave-one-out cross validation, achieving a dice overlap score of 0.84. we also applied our method to eight subcortical structures in mri from the internet brain segmentation repository, with results better or comparable to competing methods.
anatomically informed bayesian model selection for fmri group data analysis. a new approach for fmri group data analysis is introduced to overcome the limitations of standard voxel-based testing methods, such as statistical parametric mapping (spm). using a bayesian model selection framework, the functional network associated with a certain cognitive task is selected according to the posterior probabilities of mean region activations, given a pre-defined anatomical parcellation of the brain. this approach enables us to control a bayesian risk that balances false positives and false negatives, unlike the spm-like approach, which only controls false positives. on data from a mental calculation experiment, it detected the functional network known to be involved in number processing, whereas the spm-like approach either swelled or missed the different activation regions.
multi-level ground glass nodule detection and segmentation in ct lung images. early detection of ground glass nodule (ggn) in lung computed tomography (ct) images is important for lung cancer prognosis. due to its indistinct boundaries, manual detection and segmentation of ggn is labor-intensive and problematic. in this paper, we propose a novel multi-level learning-based framework for automatic detection and segmentation of ggn in lung ct images. our main contributions are: firstly, a multi-level statistical learning-based approach that seamlessly integrates segmentation and detection to improve the overall accuracy for ggn detection (in a subvolume). the classification is done at two levels, both voxel-level and object-level. the algorithm starts with a three-phase voxel-level classification step, using volumetric features computed per voxel to generate a ggn class-conditional probability map. ggn candidates are then extracted from this probability map by integrating prior knowledge of shape and location, and the ggn object-level classifier is used to determine the occurrence of the ggn. secondly, an extensive set of volumetric features are used to capture the ggn appearance. finally, to our best knowledge, the ggn dataset used for experiments is an order of magnitude larger than previous work. the effectiveness of our method is demonstrated on a dataset of 1100 subvolumes (100 containing ggns) extracted from about 200 subjects.