waashk/dblp huggingface dataset:
a cluster overlap measure for comparison of activations in fmri studies. most fmri studies use voxel-wise statistics to carry out intra-subject as well as inter-subject analysis. we show that statistics derived from voxel-wise comparisons are likely to be noisy and error prone, especially for inter-subject comparisons. in this paper we propose a novel metric called weighted cluster coverage to compare two activation maps. this metric is based on the intersection of spatially contiguous clusters of activations. it is found to be more robust than voxel-wise comparisons and could potentially lead to more statistical power in fmri-based group studies.
spatio-temporal reconstruction of dpet data using complex wavelet regularisation. traditionally, dynamic pet studies reconstruct temporally contiguous pet images using algorithms which ignore the inherent consistency between frames. we present a method which imposes a regularisation constraint based on wavelet denoising. this is achieved efficiently using the dual tree --- complex wavelet transform (dt-cwt) of kingsbury, which has many important advantages over the traditional discrete wavelet transform: shift invariance, implicit measure of local phase, and directional selectivity. in this paper, we apply the decomposition to the full spatio-temporal volume and use it for the reconstruction of dynamic (spatio-temporal) pet data.instead of using traditional wavelet thresholding schemes we introduce a locally defined and empirically-determined cross scale regularisation technique. we show that wavelet based regularisation has the potential to produce superior reconstructions and examine the effect various levels of boundary enhancement have on the overall images.we demonstrate that wavelet-based spatio-temporally regularised reconstructions have superior performance over conventional gaussian smoothing in simulated and clinical experiments. we find that our method outperforms conventional methods in terms of signal-to-noise ratio (snr) and mean square error (mse), and removes the need to post-smooth the reconstruction.
improved maximum a posteriori cortical segmentation by iterative relaxation of priors. thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of several diseases such as alzheimer's, huntington's, schizophrenia, as well as normal ageing. the presence of deep sulci and `collapsed gyri' (caused by the loss of tissue in patients with neurodegenerative diseases) complicates the tissue segmentation due to partial volume (pv) effects and limited resolution of mri. we extend existing work to improve the segmentation and thickness estimation in a single framework. we model the pv effect using a maximum a posteriori approach with novel iterative modification of the prior information to enhance deep sulci and gyri delineation. we use a voxel based approach to estimate thickness using the laplace equation within a lagrangian-eulerian framework leading to sub-voxel accuracy. experiments performed on a new digital phantom and on clinical alzheimer's disease mr images show improvements in both accuracy and robustness of the thickness measurements, as well as a reduction of errors in deep sulci and collapsed gyri.
brain connectivity using geodesics in hardi. we develop an algorithm for brain connectivity assessment using geodesics in hardi (high angular resolution diffusion imaging). we propose to recast the problem of finding fibers bundles and connectivity maps to the calculation of shortest paths on a riemannian manifold defined from fiber odfs computed from hardi measurements. several experiments on real data show that our method is able to segment fibers bundles that are not easily recovered by other existing methods.
airway tree extraction with locally optimal paths. this paper proposes a method to extract the airway tree from ct images by continually extending the tree with locally optimal paths. this is in contrast to commonly used region growing based approaches that only search the space of the immediate neighbors. the result is a much more robust method for tree extraction that can overcome local occlusions. the cost function for obtaining the optimal paths takes into account of an airway probability map as well as measures of airway shape and orientation derived from multi-scale hessian eigen analysis on the airway probability. significant improvements were achieved compared to a region growing based method, with up to 36% longer trees at a slight increase of false positive rate.
a shape relationship descriptor for radiation therapy planning. in this paper we address the challenge of matching patient geometry to facilitate the design of patient treatment plans in radiotherapy. to this end we propose a novel shape descriptor, the overlap volume histogram, which provides a rotation and translation invariant representation of a patient's organs at risk relative to the tumor volume. using our descriptor, it is possible to accurately identify database patients with similar constellations of organ and tumor geometries, enabling the transfer of treatment plans between patients with similar geometries. we demonstrate the utility of our method for such tasks by outperforming state of the art shape descriptors in the retrieval of patients with similar treatment plans. we also preliminarily show its potential as a quality control tool by demonstrating how it is used to identify an organ at risk whose dose can be significantly reduced.
tumor invasion margin on the riemannian space of brain fibers. gliomas are one of the most challenging tumors to treat or control locally. one of the main challenges is determining which areas of the apparently normal brain contain glioma cells, as gliomas are known to infiltrate for several centimeters beyond the clinically apparent lesion visualized on standard ct or mri. to ensure that radiation treatment encompasses the whole tumour, including the cancerous cells not revealed by mri, doctors treat a volume of brain extending 2cm out from the margin of the visible tumour. this expanded volume often includes healthy, non-cancerous brain tissue.knowing that glioma cells preferentially spread along nerve fibers, we propose the use of a geodesic distance on the riemannian manifold of brain fibers to replace the euclidean distance used in clinical practice and to correctly identify the tumor invasion margin. to compute the geodesic distance we use actual dti data from patients with glioma and compare our predicted growth with follow-up mri scans. results show improvement in predicting the invasion margin when using the geodesic distance as opposed to the 2cm conventional euclidean distance.
subject-matched templates for spatial normalization. spatial normalization of images from multiple subjects is a common problem in group comparison studies, such as voxel-based and deformation-based morphometric analyses. use of a study-specific template for normalization may improve normalization accuracy over a study-independent standard template (good et al., neuroimage, 14(1):21-36, 2001). here, we develop this approach further by introducing the concept of subject-matched templates. rather than using a single template for the entire population, a different template is used for every subject, with the template matched to the subject in terms of age, sex, and potentially other parameters (e.g., disease). all subject-matched templates are created from a single generative regression model of atlas appearance, thus providing a priori template-to-template correspondence without registration. we demonstrate that such an approach is technically feasible and significantly improves spatial normalization accuracy over using a single template.
constructing a dictionary of human brain folding patterns. brain imaging provides a wealth of information that computers can explore at a massive scale. categorizing the patterns of the human cortex has been a challenging issue for neuroscience. in this paper, we propose a data mining approach leading to the construction of the first computerized dictionary of cortical folding patterns, from a database of 62 brains. the cortical folds are extracted using brainvisa open software. the standard sulci are manually identified among the folds. 32 sets of sulci covering the cortex are selected. clustering techniques are further applied to identify in each set the different patterns observed in the population. after affine global normalization, the geometric distance between sulci of two subjects is calculated using the iterative closest point (icp) algorithm. the dimension of the resulting distance matrix is reduced using isomap algorithm. finally, a dedicated hierarchical clustering algorithm is used to extract out the main patterns. this algorithm provides a score which evaluates the strengths of the patterns found. the score is used to rank the patterns for setting up a dictionary to characterize the variability of cortical anatomy.
mapping tissue optical attenuation to identify cancer using optical coherence tomography. the lymphatic system is a common route for the spread of cancer and the identification of lymph node metastases is a key task during cancer surgery. this paper demonstrates the use of optical coherence tomography to construct parametric images of lymph nodes. it describes a method to automatically estimate the optical attenuation coefficient of tissue. by mapping the optical attenuation coefficient at each location in the scan, it is possible to construct a parametric image indicating variations in tissue type. the algorithm is applied to ex vivo samples of human axillary lymph nodes and validated against a histological gold standard. results are shown illustrating the variation in optical properties between cancerous and healthy tissue.
topological characterization of signal in brain images using min-max diagrams. we present a novel computational framework for characterizing signal in brain images via nonlinear pairing of critical values of the signal. among the astronomically large number of different pairings possible, we show that representations derived from specific pairing schemes provide concise representations of the image. this procedure yields a "min-max diagram" of the image data. the representation turns out to be especially powerful in discriminating image scans obtained from different clinical populations, and directly opens the door to applications in a variety of learning and inference problems in biomedical imaging. it is noticed that this strategy significantly departs from the standard image analysis paradigm --- where the `mean' signal is used to characterize an ensemble of images. this offers robustness to noise in subsequent statistical analyses, for example; however, the attenuation of the signal content due to averaging makes it rather difficult to identify subtle variations. the proposed topologically oriented method seeks to address these limitations by characterizing and encoding topological features or attributes of the image. as an application, we have used this method to characterize cortical thickness measures along brain surfaces in classifying autistic subjects. our promising experimental results provide evidence of the power of this representation.
fast and robust 3-d mri brain structure segmentation. we present a novel method for the automatic detection and segmentation of (sub-)cortical gray matter structures in 3-d magnetic resonance images of the human brain. essentially, the method is a top-down segmentation approach based on the recently introduced concept of marginal space learning (msl). we show that msl naturally decomposes the parameter space of anatomy shapes along decreasing levels of geometrical abstraction into subspaces of increasing dimensionality by exploiting parameter invariance. at each level of abstraction, i.e., in each subspace, we build strong discriminative models from annotated training data, and use these models to narrow the range of possible solutions until a final shape can be inferred. contextual information is introduced into the system by representing candidate shape parameters with high-dimensional vectors of 3-d generalized haar features and steerable features derived from the observed volume intensities. our system allows us to detect and segment 8 (sub-)cortical gray matter structures in t1-weighted 3-d mr brain scans from a variety of different scanners in on average 13.9 sec., which is faster than most of the approaches in the literature. in order to ensure comparability of the achieved results and to validate robustness, we evaluate our method on two publicly available gold standard databases consisting of several t1-weighted 3-d brain mr scans from different scanners and sites. the proposed method achieves an accuracy better than most state-of-the-art approaches using standardized distance and overlap metrics.
weakly supervised group-wise model learning based on discrete optimization. in this paper we propose a method for the weakly supervised learning of sparse appearance models from medical image data based on markov random fields (mrf). the models are learnt from a single annotated example and additional training samples without annotations. the approach formulates the model learning as solving a set of mrfs. both the model training and the resulting model are able to cope with complex and repetitive structures. the weakly supervised model learning yields sparse mrf appearance models that perform equally well as those trained with manual annotations, thereby eliminating the need for tedious manual training supervision. evaluation results are reported for hand radiographs and cardiac mri slices.
graph-based pancreatic islet segmentation for early type 2 diabetes mellitus on histopathological tissue. it is estimated that in 2010 more than 220 million people will be affected by type 2 diabetes mellitus (t2dm). early evidence indicates that specific markers for alpha and beta cells in pancreatic islets of langerhans can be used for early t2dm diagnosis. currently, the analysis of such histological tissues is manually performed by trained pathologists using a light microscope. to objectify classification results and to reduce the processing time of histological tissues, an automated computational pathology framework for segmentation of pancreatic islets from histopathological fluorescence images is proposed. due to high variability in the staining intensities for alpha and beta cells, classical medical imaging approaches fail in this scenario.the main contribution of this paper consists of a novel graph-based segmentation approach based on cell nuclei detection with randomized tree ensembles. the algorithm is trained via a cross validation scheme on a ground truth set of islet images manually segmented by 4 expert pathologists. test errors obtained from the cross validation procedure demonstrate that the graph-based computational pathology analysis proposed is performing competitively to the expert pathologists while outperforming a baseline morphological approach.
ultrafast localization of the optic disc using dimensionality reduction of the search space. optic disc (od) localization is an important pre-processing step that significantly simplifies subsequent segmentation of the od and other retinal structures. current od localization techniques suffer from impractically-high computation times (few minutes/image). in this work, we present an ultrafast technique that requiresless than a second to localize the od. the technique is based on reducing the dimensionality of the search space by projecting the 2d image feature space onto two orthogonal (x- and y-) axes. this results in two 1d signals that can be used to determine the x- and y- coordinates of the od. image features such as retinal vessels orientation and the od brightness and shape are used in the current method. four publicly-available databases, including stare and drive, were used to evaluate the proposed technique. the od was successfully located in 330 images out of 340 images (97%) with an average computation time of 0.65 seconds.
a deformable surface model for vascular segmentation. inspired by the motion of a solid surface under liquid pressure, this paper proposes a novel deformable surface model to segment blood vessels in medical images. in the proposed model, the segmented region and the background region are respectively considered as liquid and an elastic solid. the surface of the elastic solid experiences various forces derived from the second order intensity statistics and the surface geometry. these forces cause the solid surface to deform in order to segment vascular structures in an image. the proposed model has been studied in the experiments on synthetic data and clinical data acquired by different imaging modalities. it is experimentally shown that the new model is robust to intensity contrast changes inside blood vessels and thus very suitable to perform vascular segmentation.
organ segmentation with level sets using local shape and appearance priors. organ segmentation is a challenging problem on which recent progress has been made by incorporation of local image statistics that model the heterogeneity of structures outside of an organ of interest. however, most of these methods rely on landmark based segmentation, which has certain drawbacks. we propose to perform organ segmentation with a novel level set algorithm that incorporates local statistics via a highly efficient point tracking mechanism. specifically, we compile statistics on these tracked points to allow for a local intensity profile outside of the contour and to allow for a local surface area penalty, which allows us to capture fine detail where it is expected. the local intensity and curvature models are learned through landmarks automatically embedded on the surface of the training shapes. we use parzen windows to model the internal organ intensities as one distribution since this is sufficient for most organs. in addition, since the method is based on level sets, we are able to naturally take advantage of recent work on global shape regularization. we show state-of-the-art results on the challenging problems of liver and kidney segmentation.
a conditional random field approach for coupling local registration with robust tissue and structure segmentation. we consider a general modelling strategy to handle in a unified way a number of tasks essential to mr brain scan analysis. our approach is based on the explicit definition of a conditional random field (crf) model decomposed into components to be specified according to the targeted tasks. for a specific illustration, we define a crf model that combines robust-to-noise and to nonuniformity markovian tissue and structure segmentations with local affine atlas registration. the evaluation performed on both phantoms and real 3t images shows good results and, in particular, points out the gain in introducing registration as a model component. besides, our modeling and estimation scheme provide general guidelines to deal with complex joint processes for medical image analysis.
spectral embedding based probabilistic boosting tree (sceptre): classifying high dimensional heterogeneous biomedical data. the major challenge with classifying high dimensional biomedical data is in identifying the appropriate feature representation to (a) overcome the curse of dimensionality, and (b) facilitate separation between the data classes. another challenge is to integrate information from two disparate modalities, possibly existing in different dimensional spaces, for improved classification. in this paper, we present a novel data representation, integration and classification scheme, spectral embedding based probabilistic boosting tree (sceptre), which incorporates spectral embedding (se) for data representation and integration and a probabilistic boosting tree classifier for data classification. se provides an alternate representation of the data by non-linearly transforming high dimensional data into a low dimensional embedding space such that the relative adjacencies between objects are preserved. we demonstrate the utility of sceptre to classify and integrate magnetic resonance (mr) spectroscopy (mrs) and imaging (mri) data for prostate cancer detection. area under the receiver operating curve (auc) obtained via randomized cross validation on 15 prostate mri-mrs studies suggests that (a) sceptre on mrs significantly outperforms a haar wavelets based classifier, (b) integration of mri-mrs via sceptre performs significantly better compared to using mri and mrs alone, and (c) data integration via sceptre yields superior classification results compared to combining decisions from individual classifiers (or modalities).
cross modality deformable segmentation using hierarchical clustering and learning. segmentation of anatomical objects is always a fundamental task for various clinical applications. although many automatic segmentation methods have been designed to segment specific anatomical objects in a given imaging modality, a more generic solution that is directly applicable to different imaging modalities and different deformable surfaces is desired, if attainable. in this paper, we propose such a framework, which learns from examples the spatially adaptive appearance and shape of a 3d surface (either open or closed). the application to a new object/surface in a new modality requires only the annotation of training examples. key contributions of our method include: (1) an automatic clustering and learning algorithm to capture the spatial distribution of appearance similarities/variations on the 3d surface. more specifically, the model vertices are hierarchically clustered into a set of anatomical primitives (sub-surfaces) using both geometric and appearance features. the appearance characteristics of each learned anatomical primitive are then captured through a cascaded boosting learning method. (2) to effectively incorporate non-gaussian shape priors, we cluster the training shapes in order to build multiple statistical shape models. (3) to our best knowledge, this is the first time the same segmentation algorithm has been directly employed in two very diverse applications: a. liver segmentation (closed surface) in pet-ct, in which ct has very low-resolution and low-contrast; b. distal femur (condyle) surface (open surface) segmentation in mri.
personalized modeling and assessment of the aortic-mitral coupling from 4d tee and ct. the anatomy, function and hemodynamics of the aortic and mitral valves are known to be strongly interconnected. an integrated quantitative and visual assessment of the aortic-mitral coupling may have an impact on patient evaluation, planning and guidance of minimal invasive procedures. in this paper, we propose a novel model-driven method for functional and morphological characterization of the entire aortic-mitral apparatus. a holistic physiological model is hierarchically defined to represent the anatomy and motion of the two left heart valves. robust learning-based algorithms are applied to estimate the patient-specific spatial-temporal parameters from four-dimensional tee and ct data. the piecewise affine location of the valves is initially determined over the whole cardiac cycle using an incremental search performed in marginal spaces. consequently, efficient spectrum detection in the trajectory space is applied to estimate the cyclic motion of the articulated model. finally, the full personalized surface model of the aortic-mitral coupling is constructed using statistical shape models and local spatial-temporal refinement. experiments performed on 65 4d tee and 69 4d ct sequences demonstrated an average accuracy of 1.45mm and speed of 60 seconds for the proposed approach. initial clinical validation on model-based and expert measurement showed the precision to be in the range of the inter-user variability. to the best of our knowledge this is the first time a complete model of the aortic-mitral coupling estimated from tee and ct data is proposed.