250 research outputs found
An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography
Purpose: To develop an open-source, fully-automatic deep learning algorithm,
DeepGPET, for choroid region segmentation in optical coherence tomography (OCT)
data. Methods: We used a dataset of 715 OCT B-scans (82 subjects, 115 eyes)
from 3 clinical studies related to systemic disease. Ground truth segmentations
were generated using a clinically validated, semi-automatic choroid
segmentation method, Gaussian Process Edge Tracing (GPET). We finetuned a UNet
with MobileNetV3 backbone pre-trained on ImageNet. Standard segmentation
agreement metrics, as well as derived measures of choroidal thickness and area,
were used to evaluate DeepGPET, alongside qualitative evaluation from a
clinical ophthalmologist. Results: DeepGPET achieves excellent agreement with
GPET on data from 3 clinical studies (AUC=0.9994, Dice=0.9664; Pearson
correlation of 0.8908 for choroidal thickness and 0.9082 for choroidal area),
while reducing the mean processing time per image on a standard laptop CPU from
34.49s (15.09) using GPET to 1.25s (0.10) using DeepGPET. Both
methods performed similarly according to a clinical ophthalmologist, who
qualitatively judged a subset of segmentations by GPET and DeepGPET, based on
smoothness and accuracy of segmentations. Conclusions :DeepGPET, a
fully-automatic, open-source algorithm for choroidal segmentation, will enable
researchers to efficiently extract choroidal measurements, even for large
datasets. As no manual interventions are required, DeepGPET is less subjective
than semi-automatic methods and could be deployed in clinical practice without
necessitating a trained operator. DeepGPET addresses the lack of open-source,
fully-automatic and clinically relevant choroid segmentation algorithms, and
its subsequent public release will facilitate future choroidal research both in
ophthalmology and wider systemic health.Comment: 8 pages, 2 figures, 3 tables. Currently in submission to ARVO TVST
(Association for Research in Vision and Ophthalmology, Translational Vision
Science & Technology). GitHub link to codebase provided upon publicatio
Choroidalyzer: An open-source, end-to-end pipeline for choroidal analysis in optical coherence tomography
Purpose: To develop Choroidalyzer, an open-source, end-to-end pipeline for
segmenting the choroid region, vessels, and fovea, and deriving choroidal
thickness, area, and vascular index.
Methods: We used 5,600 OCT B-scans (233 subjects, 6 systemic disease cohorts,
3 device types, 2 manufacturers). To generate region and vessel ground-truths,
we used state-of-the-art automatic methods following manual correction of
inaccurate segmentations, with foveal positions manually annotated. We trained
a U-Net deep-learning model to detect the region, vessels, and fovea to
calculate choroid thickness, area, and vascular index in a fovea-centred region
of interest. We analysed segmentation agreement (AUC, Dice) and choroid metrics
agreement (Pearson, Spearman, mean absolute error (MAE)) in internal and
external test sets. We compared Choroidalyzer to two manual graders on a small
subset of external test images and examined cases of high error.
Results: Choroidalyzer took 0.299 seconds per image on a standard laptop and
achieved excellent region (Dice: internal 0.9789, external 0.9749), very good
vessel segmentation performance (Dice: internal 0.8817, external 0.8703) and
excellent fovea location prediction (MAE: internal 3.9 pixels, external 3.4
pixels). For thickness, area, and vascular index, Pearson correlations were
0.9754, 0.9815, and 0.8285 (internal) / 0.9831, 0.9779, 0.7948 (external),
respectively (all p<0.0001). Choroidalyzer's agreement with graders was
comparable to the inter-grader agreement across all metrics.
Conclusions: Choroidalyzer is an open-source, end-to-end pipeline that
accurately segments the choroid and reliably extracts thickness, area, and
vascular index. Especially choroidal vessel segmentation is a difficult and
subjective task, and fully-automatic methods like Choroidalyzer could provide
objectivity and standardisation
Certainty of outlier and boundary points processing in data mining
Data certainty is one of the issues in the real-world applications which is
caused by unwanted noise in data. Recently, more attentions have been paid to
overcome this problem. We proposed a new method based on neutrosophic set (NS)
theory to detect boundary and outlier points as challenging points in
clustering methods. Generally, firstly, a certainty value is assigned to data
points based on the proposed definition in NS. Then, certainty set is presented
for the proposed cost function in NS domain by considering a set of main
clusters and noise cluster. After that, the proposed cost function is minimized
by gradient descent method. Data points are clustered based on their membership
degrees. Outlier points are assigned to noise cluster and boundary points are
assigned to main clusters with almost same membership degrees. To show the
effectiveness of the proposed method, two types of datasets including 3
datasets in Scatter type and 4 datasets in UCI type are used. Results
demonstrate that the proposed cost function handles boundary and outlier points
with more accurate membership degrees and outperforms existing state of the art
clustering methods.Comment: Conference Paper, 6 page
Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model
A two stage statistical model based on texture and shape for fully automatic choroidal segmentation of normal and pathologic eyes obtained by a 1060 nm optical coherence tomography (OCT) system is developed. A novel dynamic programming approach is implemented to determine location of the retinal pigment epithelium/ Bruch’s membrane /choriocapillaris (RBC) boundary. The choroid–sclera interface (CSI) is segmented using a statistical model. The algorithm is robust even in presence of speckle noise, low signal (thick choroid), retinal pigment epithelium (RPE) detachments and atrophy, drusen, shadowing and other artifacts. Evaluation against a set of 871 manually segmented cross-sectional scans from 12 eyes achieves an average error rate of 13%, computed per tomogram as a ratio of incorrectly classified pixels and the total layer surface. For the first time a fully automatic choroidal segmentation algorithm is successfully applied to a wide range of clinical volumetric OCT data
Joint segmentation and classification of retinal arteries/veins from fundus images
Objective Automatic artery/vein (A/V) segmentation from fundus images is
required to track blood vessel changes occurring with many pathologies
including retinopathy and cardiovascular pathologies. One of the clinical
measures that quantifies vessel changes is the arterio-venous ratio (AVR) which
represents the ratio between artery and vein diameters. This measure
significantly depends on the accuracy of vessel segmentation and classification
into arteries and veins. This paper proposes a fast, novel method for semantic
A/V segmentation combining deep learning and graph propagation.
Methods A convolutional neural network (CNN) is proposed to jointly segment
and classify vessels into arteries and veins. The initial CNN labeling is
propagated through a graph representation of the retinal vasculature, whose
nodes are defined as the vessel branches and edges are weighted by the cost of
linking pairs of branches. To efficiently propagate the labels, the graph is
simplified into its minimum spanning tree.
Results The method achieves an accuracy of 94.8% for vessels segmentation.
The A/V classification achieves a specificity of 92.9% with a sensitivity of
93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and
sensitivity, both of 91.7%.
Conclusion The results show that our method outperforms the leading previous
works on a public dataset for A/V classification and is by far the fastest.
Significance The proposed global AVR calculated on the whole fundus image
using our automatic A/V segmentation method can better track vessel changes
associated to diabetic retinopathy than the standard local AVR calculated only
around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin
Automated retinal layer segmentation and pre-apoptotic monitoring for three-dimensional optical coherence tomography
The aim of this PhD thesis was to develop segmentation algorithm adapted and optimized to retinal OCT data that will provide objective 3D layer thickness which might be used to improve diagnosis and monitoring of retinal pathologies. Additionally, a 3D stack registration method was produced by modifying an existing algorithm. A related project was to develop a pre-apoptotic retinal monitoring based on the changes in texture parameters of the OCT scans in order to enable treatment before the changes become irreversible; apoptosis refers to the programmed cell death that can occur in retinal tissue and lead to blindness. These issues can be critical for the examination of tissues within the central nervous system. A novel statistical model for segmentation has been created and successfully applied to a large data set. A broad range of future research possibilities into advanced pathologies has been created by the results obtained. A separate model has been created for choroid segmentation located deep in retina, as the appearance of choroid is very different from the top retinal layers. Choroid thickness and structure is an important index of various pathologies (diabetes etc.). As part of the pre-apoptotic monitoring project it was shown that an increase in proportion of apoptotic cells in vitro can be accurately quantified. Moreover, the data obtained indicates a similar increase in neuronal scatter in retinal explants following axotomy (removal of retinas from the eye), suggesting that UHR-OCT can be a novel non-invasive technique for the in vivo assessment of neuronal health. Additionally, an independent project within the computer science department in collaboration with the school of psychology has been successfully carried out, improving analysis of facial dynamics and behaviour transfer between individuals. Also, important improvements to a general signal processing algorithm, dynamic time warping (DTW), have been made, allowing potential application in a broad signal processing field.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Retinal thickness estimation from SD-OCT macular scans
Glaucoma, a leading cause of blindness worldwide, can be detected using retinal thicknesses from spectral-domain optical coherence tomography (SD-OCT) scans of the macula. We calculate the desired thickness maps as the distance between the inner-limiting membrane (ILM) and retinal pigmented epithelium (RPE) of the retina. To delineate these two layers, we use a set of two deformable open surfaces that are driven by intensity contrast, while preserving their shape and topology properties, i.e. local surface smoothness and inter-surface distance smoothness. To evaluate our method, qualified graders manually segmented 30 random sections from 20 OCT image stacks, in triplicate; we make comparisons with obtained ground-truth and the clinically tested Heidelberg Spectralis segmentation. We show the superiority of our method with respect to accuracy and average execution time (~7 secs), validating it as a clinical tool
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