Group-wise context selection network for choroid segmentation in optical coherence tomography

2021 ◽  
Author(s):  
Fei Shi ◽  
Xuena Cheng ◽  
Shuanglang Feng ◽  
Changqing Yang ◽  
Shengyong Diao ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
High Myopia ◽  
State Of The Art ◽  
Dice Similarity Coefficient ◽  
Target Area ◽  
Retinal Diseases ◽  
Optical Coherence ◽  
Multi Scale ◽  
Select Group ◽  
Boundary Optimization

Abstract Choroid thickness measured from optical coherence tomography (OCT) images has emerged as a vital metric in the management of retinal diseases such as high myopia. In this paper, we propose a novel group-wise context selection network (referred to as GCS-Net) to segment the choroid of either normal or high myopia eyes. To deal with the diverse choroid thickness and the variable shape of the pathological retina, GCS-Net adopts the group-wise channel dilation (GCD) module and the group-wise spatial dilation (GSD) module, which can automatically select group-wise multi-scale information under the guidance of channel attention or spatial attention, and enhance the consistency between the receptive field and the target area. Furthermore, a boundary optimization network with a new edge loss is incorporated to improve the resulting choroid boundary by deep supervision. Experimental results evaluated on a dataset composed of 1650 clinically obtained OCT B-scans show that the proposed GCS-Net can achieve a Dice similarity coefficient of 95.97±0.54%, which outperforms some state-of-the-art segmentation networks.

scholarly journals MDAN-UNet: Multi-Scale and Dual Attention Enhanced Nested U-Net Architecture for Segmentation of Optical Coherence Tomography Images

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 60 ◽  
Author(s):  
Wen Liu ◽  
Yankui Sun ◽  
Qingge Ji
Keyword(s):  
Optical Coherence Tomography ◽  
State Of The Art ◽  
Imaging Technique ◽  
Optical Coherence ◽  
Convolutional Network ◽  
Multi Scale ◽  
Segmentation Methods ◽  
Benchmark Datasets ◽  
Resolution Imaging ◽  
Layer Segmentation

Optical coherence tomography (OCT) is an optical high-resolution imaging technique for ophthalmic diagnosis. In this paper, we take advantages of multi-scale input, multi-scale side output and dual attention mechanism and present an enhanced nested U-Net architecture (MDAN-UNet), a new powerful fully convolutional network for automatic end-to-end segmentation of OCT images. We have evaluated two versions of MDAN-UNet (MDAN-UNet-16 and MDAN-UNet-32) on two publicly available benchmark datasets which are the Duke Diabetic Macular Edema (DME) dataset and the RETOUCH dataset, in comparison with other state-of-the-art segmentation methods. Our experiment demonstrates that MDAN-UNet-32 achieved the best performance, followed by MDAN-UNet-16 with smaller parameter, for multi-layer segmentation and multi-fluid segmentation respectively.

Repeatability of Spectral Domain Optical Coherence Tomography Measurements in High Myopia

2016 ◽  
Vol 25 (5) ◽  
pp. e526-e530 ◽  
Author(s):  
Harsha L. Rao ◽  
Addepalli U. Kumar ◽  
Sampath R. Bonala ◽  
Kadam Yogesh ◽  
Bodduluri Lakshmi
Keyword(s):  
Optical Coherence Tomography ◽  
High Myopia ◽  
Spectral Domain ◽  
Optical Coherence

scholarly journals Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

2020 ◽  
pp. bjophthalmol-2020-317825
Author(s):  
Yonghao Li ◽  
Weibo Feng ◽  
Xiujuan Zhao ◽  
Bingqian Liu ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
High Myopia ◽  
Large Scale ◽  
Learning System ◽  
Youden Index ◽  
Optical Coherence ◽  
Test Dataset ◽  
Independent Test ◽  
Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

scholarly journals Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Reza Mirshahi ◽  
Pasha Anvari ◽  
Hamid Riazi-Esfahani ◽  
Mahsa Sardarinia ◽  
Masood Naseripour ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Healthy Subjects ◽  
Optical Coherence Tomography Angiography ◽  
Validation Dataset ◽  
Diabetic Patients ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Avascular Zone

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

scholarly journals Geographic mapping of choroidal thickness in myopic eyes using 1050-nm spectral domain optical coherence tomography

2015 ◽  
Vol 08 (04) ◽  
pp. 1550012 ◽  
Author(s):  
Qinqin Zhang ◽  
Maureen Neitz ◽  
Jay Neitz ◽  
Ruikang K. Wang
Keyword(s):  
Optical Coherence Tomography ◽  
High Myopia ◽  
Choroidal Thickness ◽  
Retinal Pigment ◽  
Reduction Rate ◽  
Radial Distance ◽  
Spectral Domain ◽  
Temporal Region ◽  
Optical Coherence ◽  
The Mean

Purpose: To provide a geographical map of choroidal thickness (CT) around the macular region among subjects with low, moderate and high myopia. Methods: 20 myopic subjects (n = 40 eyes) without other identified pathologies participated in this study: 20 eyes of ≤ 3 diopters (D) (low myopic), 10 eyes between -3 and -6D (moderate myopic), and 10 eyes of ≥ 6D (high myopic). The mean age of subjects was 30.2 years (± 7.6 years; range, 24 to 46 years). A 1050 nm spectral-domain optical coherence tomography (SD-OCT) system, operating at 120 kHz imaging rate, was used in this study to simultaneously capture 3D anatomical images of the choroid and measure intraocular length (IOL) in the subject. The 3D OCT images of the choroid were segmented into superior, inferior, nasal and temporal quadrants, from which the CT was measured, representing radial distance between the outer retinal pigment epithelium (RPE) layer and inner scleral border. Measurements were made within concentric regions centered at fovea centralis, extended to 5 mm away from fovea at 1 mm intervals in the nasal and temporal directions. The measured IOL was the distance from the anterior cornea surface to the RPE in alignment along the optical axis of the eye. Statistical analysis was performed to evaluate CT at each geographic region and observe the relationship between CT and the degree of myopia. Results: For low myopic eyes, the IOL was measured at 24.619 ± 0.016 mm. The CT (273.85 ± 49.01 μm) was greatest under fovea as is in the case of healthy eyes. Peripheral to the fovea, the mean CT decreased rapidly along the nasal direction, reaching a minimum of 180.65 ± 58.25μm at 5 mm away from the fovea. There was less of a change in thickness from the fovea in the temporal direction reaching a minimum of 234.25 ± 42.27 μm. In contrast to the low myopic eyes, for moderate and high myopic eyes, CTs were thickest in temporal region (where CT = 194.94 ± 27.28 and 163 ± 34.89 μm, respectively). Like the low myopic eyes, moderate and high myopic eyes had thinnest CTs in the nasal region (where CT = 100.84 ± 16.75 and 86.64 ± 42.6μm, respectively). High myopic eyes had the longest mean IOL (25.983 ± 0.021mm), while the IOL of moderate myopia was 25.413 ± 0.022 mm (**p < 0.001). The CT reduction rate was calculated at 31.28 μm/D (diopter) from low to moderate myopia, whilst it is 13.49 μm/D from moderate to high myopia. The similar tendency was found for the IOL reduction rate in our study: 0.265 mm/D from low to moderate myopia, and 0.137 mm/D from moderate to high myopia. Conclusion: The CT decreases and the IOL increases gradually with the increase of myopic condition. The current results support the theory that choroidal abnormality may play an important role in the pathogenesis of myopic degeneration.

scholarly journals Comparison between Widefield Optical Coherence Tomography Devices in Eyes with High Myopia

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 658
Author(s):  
Federico Corvi ◽  
Federico Zicarelli ◽  
Matteo Airaldi ◽  
Salvatore Parrulli ◽  
Mariano Cozzi ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
High Myopia ◽  
Cross Sectional Study ◽  
Horizontal Line ◽  
Optical Coherence ◽  
Cross Sectional ◽  
Swept Source ◽  
Image Artifact ◽  
Control Subjects ◽  
And Control

Background: To compare four different optical coherence tomography (OCT) devices for visualization of retinal and subretinal layers in highly myopic eyes. Methods: In this prospective, observational, cross-sectional study, consecutive patients with high myopia and control subjects were imaged by four OCT devices: Spectralis OCT2, PlexElite 2.0 100 kHz, PlexElite 2.0 200 kHz and the Canon Xephilio OCT-S1. The acquisition protocol for comparison consisted of single vertical and horizontal line scans centered on the fovea. Comparison between the devices in the extent of visible retina, presence of conjugate image or mirror artifacts, visibility of the sclerochoroidal interface and retrobulbar tissue. Results: 30 eyes with high myopia and 30 control subjects were analyzed. The visualized RPE length was significantly different between the OCT devices with Xephilio OCT-S1 imaging the largest extent (p < 0.0001). The proportion of eyes with conjugate image artifact was significantly higher with the Spectralis OCT (p < 0.0001), and lower with the PlexElite 200 kHz (p < 0.0001). No difference in visibility of the sclerochoroidal interface was noted among instruments. The retrobulbar tissue was visible in a higher proportion of eyes using swept-source PlexElite 100 kHz and 200 kHz (p < 0.007) compared to the other devices. Conclusions: In highly myopic eyes, the four OCT devices demonstrated significant differences in the extent of the retina imaged, in the prevalence of conjugate image artifact, and in the visualization of the retrobulbar tissue.

Sign in / Sign up

Export Citation Format

Share Document