1,013 research outputs found
Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models
The health and function of tissue rely on its vasculature network to provide
reliable blood perfusion. Volumetric imaging approaches, such as multiphoton
microscopy, are able to generate detailed 3D images of blood vessels that could
contribute to our understanding of the role of vascular structure in normal
physiology and in disease mechanisms. The segmentation of vessels, a core image
analysis problem, is a bottleneck that has prevented the systematic comparison
of 3D vascular architecture across experimental populations. We explored the
use of convolutional neural networks to segment 3D vessels within volumetric in
vivo images acquired by multiphoton microscopy. We evaluated different network
architectures and machine learning techniques in the context of this
segmentation problem. We show that our optimized convolutional neural network
architecture, which we call DeepVess, yielded a segmentation accuracy that was
better than both the current state-of-the-art and a trained human annotator,
while also being orders of magnitude faster. To explore the effects of aging
and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of
cortical blood vessels in young and old mouse models of Alzheimer's disease and
wild type littermates. We found little difference in the distribution of
capillary diameter or tortuosity between these groups, but did note a decrease
in the number of longer capillary segments () in aged animals as
compared to young, in both wild type and Alzheimer's disease mouse models.Comment: 34 pages, 9 figure
Retinal Vessels Segmentation Techniques and Algorithms: A Survey
Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.https://doi.org/10.3390/app802015
A Multi-Anatomical Retinal Structure Segmentation System For Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding
Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue to detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This thesis proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images.
The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogenous anatomical structures
The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models
The segmentation of the retinal vasculature from eye fundus images represents
one of the most fundamental tasks in retinal image analysis. Over recent years,
increasingly complex approaches based on sophisticated Convolutional Neural
Network architectures have been slowly pushing performance on well-established
benchmark datasets. In this paper, we take a step back and analyze the real
need of such complexity. Specifically, we demonstrate that a minimalistic
version of a standard U-Net with several orders of magnitude less parameters,
carefully trained and rigorously evaluated, closely approximates the
performance of current best techniques. In addition, we propose a simple
extension, dubbed W-Net, which reaches outstanding performance on several
popular datasets, still using orders of magnitude less learnable weights than
any previously published approach. Furthermore, we provide the most
comprehensive cross-dataset performance analysis to date, involving up to 10
different databases. Our analysis demonstrates that the retinal vessel
segmentation problem is far from solved when considering test images that
differ substantially from the training data, and that this task represents an
ideal scenario for the exploration of domain adaptation techniques. In this
context, we experiment with a simple self-labeling strategy that allows us to
moderately enhance cross-dataset performance, indicating that there is still
much room for improvement in this area. Finally, we also test our approach on
the Artery/Vein segmentation problem, where we again achieve results
well-aligned with the state-of-the-art, at a fraction of the model complexity
in recent literature. All the code to reproduce the results in this paper is
released
Vascular Tree Tracking and Bifurcation Points Detection in Retinal Images Using a Hierarchical Probabilistic Model
Background and Objective
Retinal vascular tree extraction plays an important role in computer-aided diagnosis and surgical operations. Junction point detection and classification provide useful information about the structure of the vascular network, facilitating objective analysis of retinal diseases.
Methods
In this study, we present a new machine learning algorithm for joint classification and tracking of retinal blood vessels. Our method is based on a hierarchical probabilistic framework, where the local intensity cross sections are classified as either junction or vessel points. Gaussian basis functions are used for intensity interpolation, and the corresponding linear coefficients are assumed to be samples from class-specific Gamma distributions. Hence, a directed Probabilistic Graphical Model (PGM) is proposed and the hyperparameters are estimated using a Maximum Likelihood (ML) solution based on Laplace approximation.
Results
The performance of proposed method is evaluated using precision and recall rates on the REVIEW database. Our experiments show the proposed approach reaches promising results in bifurcation point detection and classification, achieving 88.67% precision and 88.67% recall rates.
Conclusions
This technique results in a classifier with high precision and recall when comparing it with Xu’s method
A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features
This paper proposes a new supervised method for blood vessel segmentation using Zernike moment-based shape descriptors. The method implements a pixel wise classification by computing a 11-D feature vector comprising of both statistical (gray-level) features and shape-based (Zernike moment) features. Also the feature set contains optimal coefficients of the Zernike Moments which were derived based on the maximum differentiability between the blood vessel and background pixels. A manually selected training points obtained from the training set of the DRIVE dataset, covering all possible manifestations were used for training the ANN-based binary classifier. The method was evaluated on unknown test samples of DRIVE and STARE databases and returned accuracies of 0.945 and 0.9486 respectively, outperforming other existing supervised learning methods. Further, the segmented outputs were able to cover thinner blood vessels better than previous methods, aiding in early detection of pathologies
Segmentation Of Retinal Blood Vessels Using A Novel Fuzzy Logic Algorithm
In this work, a rule-based method is presented for blood vessel segmentation in
digital retinal images. This method can be used in computer analyses of retinal images,
e.g., in automated screening for diabetic retinopathy. Diabetic retinopathy is the most
common diabetic eye disease and a leading cause of blindness. Diagnosis of diabetic
retinopathy at an early stage can be done through the segmentation of the blood vessels of
retina. Many studies have been carried out in the last decade in order to obtain accurate
blood vessel segmentation in retinal images including supervised and rule-based methods.
This method uses eight feature vectors for each pixel. These features are means and
medians of intensity values of pixel itself, first and second nearest neighbor at four
directions. Features are used in fuzzy logic algorithm as crisp input. The final segmentation
is obtained using a thresholding method. The method was tested on the publicly available
database DRIVE and its results are compared with distinguished published methods. Our
method achieved an average accuracy of 93.82% and an area under the receiver operating
characteristic curve of 94.19% for DRIVE database. Our results demonstrated an average
sensitivity of 72.28% and a specificity of 97.04%. The calculated sensitivity and specificity values for DRIVE database also state that the proposed segmentation method is effective
and robust
Development and evaluation of a novel method for in-situ medical image display
Three-dimensional (3D) medical imaging, including computed tomography (CT) and magnetic resonance (MR), and other modalities, has become a standard of care for diagnosis of disease and guidance of interventional procedures. As the technology to acquire larger, more magnificent, and more informative medical images advances, so too must the technology to display, interact with, and interpret these data.This dissertation concerns the development and evaluation of a novel method for interaction with 3D medical images called "grab-a-slice," which is a movable, tracked stereo display. It is the latest in a series of displays developed in our laboratory that we describe as in-situ, meaning that the displayed image is embedded in a physical 3D coordinate system. As the display is moved through space, a continuously updated tomographic slice of a 3D medical image is shown on the screen, corresponding to the position and orientation of the display. The act of manipulating the display through a "virtual patient" preserves the perception of 3D anatomic relationships in a way that is not possible with conventional, fixed displays. The further addition of stereo display capabilities permits augmentation of the tomographic image data with out-of-plane structures using 3D graphical methods.In this dissertation we describe the research and clinical motivations for such a device. We describe the technical development of grab-a-slice as well as psychophysical experiments to evaluate the hypothesized perceptual and cognitive benefits. We speculate on the advantages and limitations of the grab-a-slice display and propose future directions for its use in psychophysical research, clinical settings, and image analysis
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