27 research outputs found
A (Near) Real-Time Simulation Method of Aneurysm Coil Embolization
International audienceA (Near) Real-Time Simulation Method of Aneurysm Coil Embolizatio
A framework for intracranial saccular aneurysm detection and quantification using morphological analysis of cerebral angiograms
Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively
Aneurysm Simulation and ELISA Detection in Rabbits
This Major Qualifying Project investigated blood testing as a noninvasive detection method and evaluation of the risk of Unruptured Intracranial Aneurysms (UIAs). By mimicking hypertension with manipulation of blood pressure and vascular wall repair inhibitors in the rabbit samples, it was hypothesized that there would be an increase in antibodies produced which will readily bind to the AFHYESQ peptide. Our results show a positive correlation between increase in blood pressure and increase in titer, which indicate more presence of bound antibodies, showing a more prevalent immune response to the AT1R. An ELISA assay was used for all serum samples, which can now be expanded to other models for continuous sampling
Mathematical Morphology for Quantification in Biological & Medical Image Analysis
Mathematical morphology is an established field of image processing first introduced as an application of set and lattice theories. Originally used to characterise particle distributions, mathematical morphology has gone on to be a core tool required for such important analysis methods as skeletonisation and the watershed transform. In this thesis, I introduce a selection of new image analysis techniques based on mathematical morphology.
Utilising assumptions of shape, I propose a new approach for the enhancement of vessel-like objects in images: the bowler-hat transform. Built upon morphological operations, this approach is successful at challenges such as junctions and robust against noise. The bowler-hat transform is shown to give better results than competitor methods on challenging data such as retinal/fundus imagery.
Building further on morphological operations, I introduce two novel methods for particle and blob detection. The first of which is developed in the context of colocalisation, a standard biological assay, and the second, which is based on Hilbert-Edge Detection And Ranging (HEDAR), with regard to nuclei detection and counting in fluorescent microscopy. These methods are shown to produce accurate and informative results for sub-pixel and supra-pixel object counting in complex and noisy biological scenarios.
I propose a new approach for the automated extraction and measurement of object thickness for intricate and complicated vessels, such as brain vascular in medical images. This pipeline depends on two key technologies: semi-automated segmentation by advanced level-set methods and automatic thickness calculation based on morphological operations. This approach is validated and results demonstrating the broad range of challenges posed by these images and the possible limitations of this pipeline are shown.
This thesis represents a significant contribution to the field of image processing using mathematical morphology and the methods within are transferable to a range of complex challenges present across biomedical image analysis
Volumetric velocimetry for fluid flows
In recent years, several techniques have been introduced that are capable of extracting 3D three-component velocity fields in fluid flows. Fast-paced developments in both hardware and processing algorithms have generated a diverse set of methods, with a growing range of applications in flow diagnostics. This has been further enriched by the increasingly marked trend of hybridization, in which the differences between techniques are fading. In this review, we carry out a survey of the prominent methods, including optical techniques and approaches based on medical imaging. An overview of each is given with an example of an application from the literature, while focusing on their respective strengths and challenges. A framework for the evaluation of velocimetry performance in terms of dynamic spatial range is discussed, along with technological trends and emerging strategies to exploit 3D data. While critical challenges still exist, these observations highlight how volumetric techniques are transforming experimental fluid mechanics, and that the possibilities they offer have just begun to be explored.SD was partially supported under Grant No. DPI2016-79401-R funded by the Spanish State Research Agency (SRA) and the European Regional Development Fund (ERDF). FC was partially supported by the U.S. National Science Foundation (Chemical, Bioengineering, Environmental, and Transport Systems, Grant No. 1453538)
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A recommended “minimum data set” framework for SD-OCT retinal image acquisition and analysis from the Atlas of Retinal Imaging in Alzheimer’s Study (ARIAS)
Introduction: We propose a minimum data set framework for the acquisition and analysis of retinal images for the development of retinal Alzheimer\u27s disease (AD) biomarkers. Our goal is to describe methodology that will increase concordance across laboratories, so that the broader research community is able to cross‐validate findings in parallel, accumulate large databases with normative data across the cognitive aging spectrum, and progress the application of this technology from the discovery stage to the validation stage in the search for sensitive and specific retinal biomarkers in AD.
Methods: The proposed minimum data set framework is based on the Atlas of Retinal Imaging Study (ARIAS), an ongoing, longitudinal, multi‐site observational cohort study. However, the ARIAS protocol has been edited and refined with the expertise of all co‐authors, representing 16 institutions, and research groups from three countries, as a first step to address a pressing need identified by experts in neuroscience, neurology, optometry, and ophthalmology at the Retinal Imaging in Alzheimer\u27s Disease (RIAD) conference, convened by the Alzheimer\u27s Association and held in Washington, DC, in May 2019.
Results: Our framework delineates specific imaging protocols and methods of analysis for imaging structural changes in retinal neuronal layers, with optional add‐on procedures of fundus autofluorescence to examine beta‐amyloid accumulation and optical coherence tomography angiography to examine AD‐related changes in the retinal vasculature.
Discussion: This minimum data set represents a first step toward the standardization of retinal imaging data acquisition and analysis in cognitive aging and AD. A standardized approach is essential to move from discovery to validation, and to examine which retinal AD biomarkers may be more sensitive and specific for the different stages of the disease severity spectrum. This approach has worked for other biomarkers in the AD field, such as magnetic resonance imaging; amyloid positron emission tomography; and, more recently, blood proteomics. Potential context of use for retinal AD biomarkers is discussed
Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation
Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community