Iterative Kernel PCA based Classification of Retinal Images for Diabetic Macular Edema

2013 ◽  
Vol 67 (8) ◽  
pp. 22-26
Author(s):  
Vipin KrishnanCV ◽  
V. S. Jayanthi ◽  
Jestin V. Kunjummen
2018 ◽  
Vol 46 (11) ◽  
pp. 4455-4464 ◽  
Author(s):  
Young Joo Cho ◽  
Dong Hyun Lee ◽  
Min Kim

Objective To evaluate the short-term efficacy of intravitreal bevacizumab (IVB) and posterior sub-tenon triamcinolone injections (PSTI) on the basis of spectral-domain optical coherence tomography (SD-OCT) patterns in diabetic macular edema (DME). Methods We retrospectively reviewed 73 eyes of 73 patients with DME. Based on the presence of serous retinal detachment (SRD), eyes were categorized into two groups, and either IVB or PSTI treatment was performed. Central macular thickness (CMT) and the degree of SRD were assessed preoperatively and 1 month postoperatively. The severity of intraretinal edema was approximated based on the distance from the external limiting membrane to the internal limiting membrane. Results In eyes with SRD, reduction of SRD was greater with IVB than with PSTI. Moreover, reduction of intraretinal edema was greater with PSTI than with IVB. In eyes without SRD, PSTI achieved greater CMT reduction, compared with IVB. Conclusions In DME patients with SRD, IVB achieved greater reduction of SRD, compared with PSTI; however, intraretinal edema responded more favorably to PSTI, regardless of the presence of SRD. Our results suggest that the classification of DME based on OCT findings may be useful to predict responses to IVB or PSTI treatments.


2021 ◽  
Author(s):  
Fangyao Tang ◽  
Xi Wang ◽  
An-ran Ran ◽  
Carmen KM Chan ◽  
Mary Ho ◽  
...  

<a><b>Objective:</b></a> Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep-learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. <p><b>Research Design and Methods:</b> We trained and validated two versions of a multi-task convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume-scans and two-dimensional (2D) B-scans respectively. For both 3D and 2D CNNs, we employed the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent datasets from Singapore, Hong Kong, the US, China, and Australia. </p> <p><b>Results:</b> In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920–0.954), 0.958 (0.930–0.977), and 0.965 (0.948–0.977) for primary dataset obtained from Cirrus, Spectralis, and Triton OCTs respectively, in addition to AUROCs greater than 0.906 for the external datasets. For the further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940–0.995), 0.951 (0.898–0.982), and 0.975 (0.947–0.991) for the primary dataset and greater than 0.894 for the external datasets. </p> <p><b>Conclusion:</b> We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics. </p>


2020 ◽  
Vol 155 (5) ◽  
Author(s):  
Sergio Hernández-Da Mota ◽  
Virgilio Lima-Gómez ◽  
Ernesto Rodríguez-Ayala ◽  
Jorge Jans Fromow-Guerra ◽  
Enrique Alfonso Roig Melo-Granados

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Yoshihiro Takamura ◽  
Takehiro Matsumura ◽  
Shogo Arimura ◽  
Makoto Gozawa ◽  
Masakazu Morioka ◽  
...  

Purpose. To introduce a novel laser photocoagulation (PC) protocol named merged image-guided PC (MIG-PC), which included merging the images of the fundus, optical coherence tomography (OCT) map, and fluorescein angiography (FA). We compared the anatomical and functional results between MIG-PC and FA-guided PC (FG-PC) for the treatment of focal diabetic macular edema (DME). Method. We examined the treatment outcomes in 27 consecutive eyes treated with MIG-PC compared with 28 matched eyes treated with FG-PC. We identified the microaneurysms (MAs) located in the focal edema areas and ablated them using focal PC. Best-corrected visual acuity (BCVA) and retinal thickness (RT) measured using OCT were compared between the groups at baseline and 2, 4, 8, 12, and 24 weeks after treatment. Results. The foveal and perifoveal RT were reduced after treatment in both the groups, and the perifoveal RT in the MIG-PC group was significantly lower than that in the FG-PC group at 4 weeks and thereafter. BCVA in the MIG-PC group was significantly higher than that in the FG-PC group at 12 and 24 weeks. The numbers of laser spots (p=0.0001), additional laser treatments (p=0.0121), and intravitreal injection of ranibizumab (p=0.0012) in the MIG-PC group were significantly lower than those in the FG-PC group (Mann–Whitney test). Conclusion. MIG-PC contributed to the improvement in BCVA and reduction in RT, number of laser shots required, and retreatment rates. Based on our data, MIG-PC can be recommended for the treatment of focal DME. This trial is registered with ID UMIN000030390.


2019 ◽  
Vol 1368 ◽  
pp. 032014 ◽  
Author(s):  
N Yu Ilyasova ◽  
A S Shirokanev ◽  
N S Demin ◽  
E A Zamyckij

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