383 research outputs found
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Fast Re-Rendering of Volume and Surface Graphics by Depth, Color, and Opacity Buffering
A method for quickly re-rendering volume data consisting of several distinct materials and intermixed with moving geometry is presented. The technique works by storing depth, color and opacity information, to a given approximation, which facilitates accelerated rendering of fixed views at moderate storage overhead without re-scanning the entire volume. Storage information in the ray direction (what we have called super-z depth buffering), allows rapid transparency and color changes of materials, position changes of sub-objects, dealing explicitly with regions of overlap, and the intermixing or separately rendered geometry. The rendering quality can be traded-off against the relative storage cost and we present an empirical analysis of output error together with typical figures for its storage complexity. The method has been applied to the visualization of medical image data for surgical planning and guidance, and presented results include typical clinical data. We discuss the implications of our method for haptic (or tactile) rendering systems, such as for surgical simulation, and present preliminary results of rendering polygonal objects in the volume rendered scene.Engineering and Applied Science
Pituitary Adenoma Volumetry with 3D Slicer
In this study, we present pituitary adenoma volumetry using the free and open source medical image computing platform for biomedical research: (3D) Slicer. Volumetric changes in cerebral pathologies like pituitary adenomas are a critical factor in treatment decisions by physicians and in general the volume is acquired manually. Therefore, manual slice-by-slice segmentations in magnetic resonance imaging (MRI) data, which have been obtained at regular intervals, are performed. In contrast to this manual time consuming slice-by-slice segmentation process Slicer is an alternative which can be significantly faster and less user intensive. In this contribution, we compare pure manual segmentations of ten pituitary adenomas with semi-automatic segmentations under Slicer. Thus, physicians drew the boundaries completely manually on a slice-by-slice basis and performed a Slicer-enhanced segmentation using the competitive region-growing based module of Slicer named GrowCut. Results showed that the time and user effort required for GrowCut-based segmentations were on average about thirty percent less than the pure manual segmentations. Furthermore, we calculated the Dice Similarity Coefficient (DSC) between the manual and the Slicer-based segmentations to proof that the two are comparable yielding an average DSC of 81.97±3.39%
A CT Database for Research, Development and Education: Concept and Potential
Both in radiology and in surgery, numerous applications are emerging that enable 3D visualization of data from various imaging modalities. In clinical practice, the patient's images are analyzed on work stations in the Radiology Department. For specific preclinical and educational applications, however, data from single patients are insufficient. Instead, similar scans from a number of individuals within a collective must be compiled. The definition of standardized acquisition procedures and archiving formats are prerequisite for subsequent analysis of multiple data sets. Focusing on bone morphology, we describe our concept of a computer database of 3D human bone models obtained from computed tomography (CT) scans. We further discuss and illustrate deployment areas ranging from prosthesis design, over virtual operation simulation up to 3D anatomy atlases. The database of 3D bone models described in this work, created and maintained by the AO Development Institute, may be accessible to research institutes on reques
Subject-centered multi-view feature fusion for neuroimaging retrieval and classification
Multi-View neuroimaging retrieval and classification play an important role in computer-aided-diagnosis of brain disorders, as multi-view features could provide more insights of the disease pathology and potentially lead to more accurate diagnosis than single-view features. The large inter-feature and inter-subject variations make the multi-view neuroimaging analysis a challenging task. Many multi-view or multi-modal feature fusion methods have been proposed to reduce the impact of inter-feature variations in neuroimaging data. However, there is not much in-depth work focusing on the inter-subject variations. In this study, we propose a subject-centered multi-view feature fusion method for neuroimaging retrieval and classification based on the propagation graph fusion (PGF) algorithm. Two main advantages of the proposed method are: 1) it evaluates the query online and adaptively reshapes the connections between subjects according to the query; 2) it measures the affinity of the query to the subjects using the subject-centered affinity matrices, which can be easily combined and efficiently solved. Evaluated using a public accessible neuroimaging database, our algorithm outperforms the state-of-the-art methods in retrieval and achieves comparable performance in classification
Co-dimension 2 Geodesic Active Contours for MRA Segmentation
Automatic and semi-automatic magnetic resonance angiography (MRA)s segmentation techniques can potentially save radiologists larges amounts of time required for manual segmentation and cans facilitate further data analysis. The proposed MRAs segmentation method uses a mathematical modeling technique whichs is well-suited to the complicated curve-like structure of bloods vessels. We define the segmentation task as ans energy minimization over all 3D curves and use a level set methods to search for a solution. Ours approach is an extension of previous level set segmentations techniques to higher co-dimension
Longitudinal brain MR retrieval with diffeomorphic demons registration: What happened to those patients with similar changes?
Current medical content-based retrieval (MCBR) systems for neuroimaging data mainly focus on retrieving the cross-sectional neuroimaging data with similar regional or global measurements. The longitudinal pathological changes along different time-points are usually neglected in such MCBR systems. We propose the cross-registration based retrieval for longitudinal MR data to retrieve patients with similar structural changes as an extension to the existing MCBR systems. The diffeomorphic demons registration is used to extract the tissue deformation between two adjacent MR volumes. An asymmetric square dissimilarity matrix is designed for indexing the patient changes within a specific interval. A visual demonstration is given to show the registration displacement fields of the query as compared to the simulated results. The experimental performance with the mean average precision (mAP) and the average top-K accuracy (aACC) are reported for evaluation
Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.
Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome
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Detection and analysis of statistical differences in anatomical shape
We present a computational framework for image-based analysis and interpretation of statistical differences in anatomical shape between populations. Applications of such analysis include understanding developmental and anatomical aspects of disorders when comparing patients vs. normal controls, studying morphological changes caused by aging, or even differences in normal anatomy, for example, differences between genders. Once a quantitative description of organ shape is extracted from input images, the problem of identifying differences between the two groups can be reduced to one of the classical questions in machine learning of constructing a classifier function for assigning new examples to one of the two groups while making as few misclassifications as possible. The resulting classifier must be interpreted in terms of shape differences between the two groups back in the image domain. We demonstrate a novel approach to such interpretation that allows us to argue about the identified shape differences in anatomically meaningful terms of organ deformation. Given a classifier function in the feature space, we derive a deformation that corresponds to the differences between the two classes while ignoring shape variability within each class. Based on this approach, we present a system for statistical shape analysis using distance transforms for shape representation and the Support Vector Machines learning algorithm for the optimal classifier estimation and demonstrate it on artificially generated data sets, as well as real medical studies
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