23 research outputs found
CUP and DISC OPTIC Segmentation Using Optimized Superpixel Classification for Glaucoma Screening
Glaucoma, an incurable disease related to eyes which results in loss of the vision. Identifying this disease within in a proper period of time is most important, since it cannot be cured. The important aspect of this paper is to detect glaucoma at initial stages. Segmentation in the optic disc necessitates the differentiation of each super pixel by employing Histograms, centre surround statistics. Information location in merged with the above methods in increasing the performance of optic cup segmentation. Optic disc and optic cups are employed to evaluate cup to disc ratio of the disease identified. Neural network is used to extract the patterns and also to detect glaucomatous cells that are too complex to be noticed by either humans or other computer techniques
An automated framework of inner segment/outer segment defect detection for retinal SD-OCT images
The integrity of inner segment/outer segment (IS/OS) has high correlation with lower visual acuity in patients suffering from blunt trauma. An automated 3D IS/OS defect detection method based on the SD-OCT images was proposed. First, 11 surfaces were automatically segmented using the multiscale 3D graph-search approach. Second, the sub-volumes between surface 7 and 8 containing IS/OS region around the fovea (diameter of mm) were extracted and flattened based on the segmented retinal pigment epithelium layer. Third, 5 kinds of texture based features were extracted for each voxel. A KNN classifier was trained and each voxel was classified as disrupted or nondisrupted and the responding defect volume was calculated. The proposed method was trained and tested on 9 eyes from 9 trauma subjects using the leave-one-out cross validation method. The preliminary results demonstrated the feasibility and efficiency of the proposed method
Predicting Drusen Regression from OCT in Patients with Age-Related Macular Degeneration
Age-related macular degeneration (AMD) is a leading cause of blindness in developed countries. The presence of drusen is the hallmark of early/intermediate AMD, and their sudden regression is strongly associated with the onset of late AMD. In this work we propose a predictive model of drusen regression using optical coherence tomography (OCT) based features. First, a series of automated image analysis steps are applied to segment and characterize individual drusen and their development. Second, from a set of quantitative features, a random forest classifiser is employed to predict the occurrence of individual drusen regression within the following 12 months. The predictive model is trained and evaluated on a longitudinal OCT dataset of 44 eyes from 26 patients using leave-one-patient-out cross-validation. The model achieved an area under the ROC curve of 0.81, with a sensitivity of 0.74 and a specificity of 0.73. The presence of hyperreflective foci and mean drusen signal intensity were found to be the two most important features for the prediction. This preliminary study shows that predicting drusen regression is feasible and is a promising step toward identification of imaging biomarkers of incoming regression
An automated framework of inner segment/outer segment defect detection for retinal SD-OCT images
The integrity of inner segment/outer segment (IS/OS) has high correlation with lower visual acuity in patients suffering from blunt trauma. An automated 3D IS/OS defect detection method based on the SD-OCT images was proposed. First, 11 surfaces were automatically segmented using the multiscale 3D graph-search approach. Second, the sub-volumes between surface 7 and 8 containing IS/OS region around the fovea (diameter of mm) were extracted and flattened based on the segmented retinal pigment epithelium layer. Third, 5 kinds of texture based features were extracted for each voxel. A KNN classifier was trained and each voxel was classified as disrupted or nondisrupted and the responding defect volume was calculated. The proposed method was trained and tested on 9 eyes from 9 trauma subjects using the leave-one-out cross validation method. The preliminary results demonstrated the feasibility and efficiency of the proposed method
Multimodal Graph-Theoretic Approach for Segmentation of the Internal Limiting Membrane at the Optic Nerve Head
In this work, we present a multimodal multiresolution graph-based method to segment the top surface of the retina called the internal limiting membrane (ILM) within optic-nerve-head-centered spectral-domain optical coherence tomography (SD-OCT) volumes. Having a precise ILM surface is crucial as this surface is utilized for measuring several structural parameters such as Bruch’s membrane opening-minimum rim width (BMO-MRW) and cup volume. The proposed method addresses the common current segmentation errors due to the presence of retinal blood vessels, deep cupping, or a very steep slope of the ILM. In order to resolve these issues, the volume is resampled using a set of gradient vector flow (GVF) based columns. The GVF field is computed according to an initial surface segmentation which is obtained through a multiresolution framework. The retinal blood vessel information (obtained from corresponding registered fundus photographs) along with shape prior information are incorporated in a graph-theoretic approach to compute the ILM segmentation. The method is tested on the SD-OCT volumes from 44 glaucoma subjects and significantly smaller errors were obtained than that from current approaches
Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
Noisy data and the similarity in the ocular appearances caused by different
ophthalmic pathologies pose significant challenges for an automated expert
system to accurately detect retinal diseases. In addition, the lack of
knowledge transferability and the need for unreasonably large datasets limit
clinical application of current machine learning systems. To increase
robustness, a better understanding of how the retinal subspace deformations
lead to various levels of disease severity needs to be utilized for
prioritizing disease-specific model details. In this paper we propose the use
of disease-specific feature representation as a novel architecture comprised of
two joint networks -- one for supervised encoding of disease model and the
other for producing attention maps in an unsupervised manner to retain disease
specific spatial information. Our experimental results on publicly available
datasets show the proposed joint-network significantly improves the accuracy
and robustness of state-of-the-art retinal disease classification networks on
unseen datasets.Comment: \c{opyright} 2020 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
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this work in other work
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
Diagnosing different retinal diseases from Spectral Domain Optical Coherence
Tomography (SD-OCT) images is a challenging task. Different automated
approaches such as image processing, machine learning and deep learning
algorithms have been used for early detection and diagnosis of retinal
diseases. Unfortunately, these are prone to error and computational
inefficiency, which requires further intervention from human experts. In this
paper, we propose a novel convolution neural network architecture to
successfully distinguish between different degeneration of retinal layers and
their underlying causes. The proposed novel architecture outperforms other
classification models while addressing the issue of gradient explosion. Our
approach reaches near perfect accuracy of 99.8% and 100% for two separately
available Retinal SD-OCT data-set respectively. Additionally, our architecture
predicts retinal diseases in real time while outperforming human
diagnosticians.Comment: 8 pages. Accepted to 18th IEEE International Conference on Machine
Learning and Applications (ICMLA 2019
3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement
With the introduction of spectral-domain optical coherence tomography
(SDOCT), much larger image datasets are routinely acquired compared to what was
possible using the previous generation of time-domain OCT. Thus, there is a
critical need for the development of 3D segmentation methods for processing
these data. We present here a novel 3D automatic segmentation method for
retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume
datasets are obtained by using a 3D smoothing filter and a 3D differential
filter. Their linear combination is then calculated to generate new volume data
with an enhanced boundary surface, where pixel intensity, boundary position
information, and intensity changes on both sides of the boundary surface are
used simultaneously. Next, preliminary discrete boundary points are detected
from the A-Scans of the volume data. Finally, surface smoothness constraints
and a dynamic threshold are applied to obtain a smoothed boundary surface by
correcting a small number of error points. Our method can extract retinal layer
boundary surfaces sequentially with a decreasing search region of volume data.
We performed automatic segmentation on eight human OCT volume datasets acquired
from a commercial Spectralis OCT system, where each volume of data consisted of
97 OCT images with a resolution of 496 512; experimental results show that this
method can accurately segment seven layer boundary surfaces in normal as well
as some abnormal eyes.Comment: 27 pages, 19 figure
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FloatingCanvas: quantification of 3D retinal structures from spectral-domain optical coherence tomography
Spectral-domain optical coherence tomography (SD-OCT) provides volumetric images of retinal structures with unprecedented detail. Accurate segmentation algorithms and feature quantification in these images, however, are needed to realize the full potential of SD-OCT. The fully automated segmentation algorithm, FloatingCanvas, serves this purpose and performs a volumetric segmentation of retinal tissue layers in three-dimensional image volume acquired around the optic nerve head without requiring any pre-processing. The reconstructed layers are analysed to extract features such as blood vessels and retinal nerve fibre layer thickness. Findings from images obtained with the RTVue-100 SD-OCT (Optovue, Fremont, CA, USA) indicate that FloatingCanvas is computationally efficient and is robust to the noise and low contrast in the images. The FloatingCanvas segmentation demonstrated good agreement with the human manual grading. The retinal nerve fibre layer thickness maps obtained with this method are clinically realistic and highly reproducible compared with time-domain StratusOCT™