scholarly journals A Joint Model for Macular Edema Analysis in Optical Coherence Tomography Images Based on Image Enhancement and Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhifu Tao ◽  
Wenping Zhang ◽  
Mudi Yao ◽  
Yuanfu Zhong ◽  
Yanan Sun ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Macular Edema ◽  
Image Enhancement ◽  
Level Set ◽  
Processing Time ◽  
Similarity Coefficient ◽  
Kappa Coefficient ◽  
Manual Delineation ◽  
Proposed Model ◽  
Sensitivity Specificity

Optical coherence tomography (OCT) provides the visualization of macular edema which can assist ophthalmologists in the diagnosis of ocular diseases. Macular edema is a major cause of vision loss in patients with retinal vein occlusion (RVO). However, manual delineation of macular edema is a laborious and time-consuming task. This study proposes a joint model for automatic delineation of macular edema in OCT images. This model consists of two steps: image enhancement using a bioinspired algorithm and macular edema segmentation using a Gaussian-filtering regularized level set (SBGFRLS) algorithm. We then evaluated the delineation efficiency using the following parameters: accuracy, precision, sensitivity, specificity, Dice’s similarity coefficient, IOU, and kappa coefficient. Compared with the traditional level set algorithms, including C-V and GAC, the proposed model had higher efficiency in macular edema delineation as shown by reduced processing time and iteration times. Moreover, the accuracy, precision, sensitivity, specificity, Dice’s similarity coefficient, IOU, and kappa coefficient for macular edema delineation could reach 99.7%, 97.8%, 96.0%, 99.0%, 96.9%, 94.0%, and 96.8%, respectively. More importantly, the proposed model had comparable precision but shorter processing time compared with manual delineation. Collectively, this study provides a novel model for the delineation of macular edema in OCT images, which can assist the ophthalmologists for the screening and diagnosis of retinal diseases.

scholarly journals EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

2018 ◽  
Vol 7 (2) ◽  
pp. 7 ◽  
Author(s):  
S Subasree ◽  
N P Gopalan ◽  
N K Sakthivel
Keyword(s):  
Classification Accuracy ◽  
Processing Time ◽  
Research Work ◽  
Time Processing ◽  
Multi Objective ◽  
Time Sensitivity ◽  
Proposed Model ◽  
Sensitivity Specificity ◽  
Cancer Pattern ◽  
Memory Utilization

Microarray based Cancer Pattern Classification is one of the popular techniques in Bioinformatics Research. This Research Work is noticed that for studying the expression levels through the Gene Expression profiling experiments, thousands of Genes have to be simultaneously studied to understand the patterns of the Gene Expression or Cancer Pattern. This research work proposed an efficient Cancer Pattern Clas-sifier called An Enhanced Multi-Objective Pswarm (EMOPS) and it is studied thoroughly in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. The results were compared with the recently proposed classifiers namely Hybrid Ant Bee Algorithm (HABA), Kernelized Fuzzy Rough Set Based Semi Supervised Support Vector Machine (KFRS-S3VM) and Multi-objective Particle Swarm Optimization (MPSO). For analyzing the performances of the proposed model, this work considered a few cancer patterns namely Bladder, Breast, Colon, Endometrial, Kidney, Leukemia, Lung, Melanoma, Mom-Hodgkin, Pancreatic, Prostate and Thyroid. From our experimental results, it was noticed that the proposed model outperforms the identified three classifiers in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. To improve the performance of the system further in term of Processing Time, the proposed model Enhanced Multi-Objective Pswarm (EMOPS) is implemented under Parallel Framework and evaluated. That is the model is tested with Two, Four, Eight and Sixteen Parallel Processors and from the results, it is established that the Processing Time decreases considerably which will improve the performance of the Proposed Model.

scholarly journals Detection of Diabetic Macular Edema in Optical Coherence Tomography Image Using an Improved Level Set Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Zhenhua Wang ◽  
Wenping Zhang ◽  
Yanan Sun ◽  
Mudi Yao ◽  
Biao Yan
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Level Set ◽  
Diabetic Patients ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Oct Image ◽  
Novel Algorithm

Diabetic macular edema (DME) is a major cause of visual loss in the patients with diabetic retinopathy. DME detection in Optical Coherence Tomography (OCT) image contributes to the early diagnosis of diabetic retinopathy and blindness prevention. Currently, DME detection in the OCT image mainly relies on the handwork by the experienced clinician. It is a laborious, time-consuming, and challenging work to organize a comprehensive DME screening for diabetic patients. In this study, we proposed a novel algorithm for the detection and segmentation of DME region in OCT image based on the K-means clustering algorithm and improved Selective Binary and Gaussian Filtering regularized level set (SBGFRLS) algorithm named as SBGFRLS-OCT algorithm. SBGFRLS-OCT algorithm was compared with the current level set algorithms, including C-V (Chan-Vese), GAC (geodesic active contour), and SBGFRLS, to estimate the performance of DME detection. SBGFRLS-OCT algorithm was also compared with the clinician to estimate the precision, sensitivity, and specificity of DME segmentation. Compared with C-V, GAC, and SBGFRLS algorithm, the SBGFRLS-OCT algorithm enhanced the accuracy and reduces the processing time of DME detection. Compared with manual DME segmentation, the SBGFRLS-OCT algorithm achieved a comparable precision (97.7%), sensitivity (91.8%), and specificity (99.2%). Collectively, this study presents a novel algorithm for DME detection in the OCT image, which can be used for mass diabetic retinopathy screening.

Cystoid Macular Edema Secondary to Paclitaxel Therapy

2019 ◽  
Vol 2 ◽  
pp. 1 ◽  
Author(s):  
Anibal Martin Folgar ◽  
Jorge Oscar Zarate
Keyword(s):  
Optical Coherence Tomography ◽  
Visual Acuity ◽  
Fluorescein Angiography ◽  
Macular Edema ◽  
Cystoid Macular Edema ◽  
Metastatic Breast ◽  
Breast Adenocarcinoma ◽  
Optical Coherence ◽  
Ophthalmoscopic Examination

We present a 57-year-old referred reduced visual acuity who was in treatment with paclitaxel for developing metastatic breast adenocarcinoma. Ophthalmoscopic examination, optical coherence tomography, and autofluorescence show the cystoid macular edema, but fluorescein angiography is normal, without leakage of dye in the late times. The patient responds well 8 weeks after stopping antineoplastic. Paclitaxel can cause cystoid macular edema and lifting a recovery both anatomical and functional of the macula.

scholarly journals En face image-based classification of diabetic macular edema using swept source optical coherence tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Atsushi Fujiwara ◽  
Yuki Kanzaki ◽  
Shuhei Kimura ◽  
Mio Hosokawa ◽  
Yusuke Shiode ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Outer Nuclear Layer ◽  
Subretinal Fluid ◽  
Plexiform Layer ◽  
Optical Coherence ◽  
Fiber Layer ◽  
Swept Source ◽  
En Face

AbstractThis retrospective study was performed to classify diabetic macular edema (DME) based on the localization and area of the fluid and to investigate the relationship of the classification with visual acuity (VA). The fluid was visualized using en face optical coherence tomography (OCT) images constructed using swept-source OCT. A total of 128 eyes with DME were included. The retina was segmented into: Segment 1, mainly comprising the inner nuclear layer and outer plexiform layer, including Henle’s fiber layer; and Segment 2, mainly comprising the outer nuclear layer. DME was classified as: foveal cystoid space at Segment 1 and no fluid at Segment 2 (n = 24), parafoveal cystoid space at Segment 1 and no fluid at Segment 2 (n = 25), parafoveal cystoid space at Segment 1 and diffuse fluid at Segment 2 (n = 16), diffuse fluid at both segments (n = 37), and diffuse fluid at both segments with subretinal fluid (n = 26). Eyes with diffuse fluid at Segment 2 showed significantly poorer VA, higher ellipsoid zone disruption rates, and greater central subfield thickness than did those without fluid at Segment 2 (P < 0.001 for all). These results indicate the importance of the localization and area of the fluid for VA in DME.

scholarly journals Comparison of swept-source versus spectral-domain optical coherence tomography angiography for detection of macular neovascularization

2021 ◽  
Author(s):  
Anna Lentzsch ◽  
Laura Schöllhorn ◽  
Christel Schnorr ◽  
Robert Siggel ◽  
Sandra Liakopoulos
Keyword(s):  
Optical Coherence Tomography ◽  
Optical Coherence Tomography Angiography ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Cross Sectional ◽  
Swept Source ◽  
En Face ◽  
Increased Sensitivity ◽  
Sensitivity Specificity ◽  
Subretinal Hyperreflective Material

Abstract Purpose To compare swept-source (SS) versus spectral-domain (SD) optical coherence tomography angiography (OCTA) for the detection of macular neovascularization (MNV). Methods In this prospective cohort study, 72 eyes of 54 patients with subretinal hyperreflective material (SHRM) and/or pigment epithelial detachment (PED) on OCT possibly corresponding to MNV in at least one eye were included. OCTA scans were acquired using two devices, the PLEX Elite 9000 SS-OCTA and the Spectralis SD-OCTA. Fluorescein angiography (FA) was used as reference. Two graders independently evaluated en face OCTA images using a preset slab as well as a manually modified slab, followed by a combination of en face and cross-sectional OCTA. Results Sensitivity (specificity) for the automated slabs was 51.7% (93.0%) for SS-OCTA versus 58.6% (95.3%) for SD-OCTA. Manual modification of segmentation increased sensitivity to 79.3% for SS-OCTA but not for SD-OCTA (58.6%). The combination of en face OCTA with cross-sectional OCTA reached highest sensitivity values (SS-OCTA: 82.8%, SD-OCTA: 86.2%), and lowest number of cases with discrepancies between SS-OCTA and SD-OCTA (4.2%). Fleiss kappa as measure of concordance between FA, SS-OCTA, and SD-OCTA was 0.56 for the automated slabs, 0.60 for the manual slabs, and 0.73 (good agreement) for the combination of en face OCTA with cross-sectional OCTA. Concordance to FA was moderate for the automated slabs and good for manual slabs and combination with cross-sectional OCTA of both devices. Conclusion Both devices reached comparable results regarding the detection of MNV on OCTA. Sensitivity for MNV detection and agreement between devices was best when evaluating a combination of en face and cross-sectional OCTA.

scholarly journals Self-Collection of Saliva Specimens as a Suitable Alternative to Nasopharyngeal Swabs for the Diagnosis of SARS-CoV-2 by RT-qPCR

2021 ◽  
Vol 10 (2) ◽  
pp. 299
Author(s):  
Camino Trobajo-Sanmartín ◽  
Marta Adelantado ◽  
Ana Navascués ◽  
María J. Guembe ◽  
Isabel Rodrigo-Rincón ◽  
...  
Keyword(s):  
Chronic Conditions ◽  
Saliva Sample ◽  
Virus Detection ◽  
Kappa Coefficient ◽  
Nasopharyngeal Swab ◽  
Personal Protection ◽  
Personal Protection Equipment ◽  
Suitable Alternative ◽  
Lysis Buffer ◽  
Sensitivity Specificity

A nasopharyngeal swab is a sample used for the diagnosis of SARS-CoV-2 infection. Saliva is a sample easier to obtain and the risk of contagion for the professional is lower. This study aimed to evaluate the utility of saliva for the diagnosis of SARS-CoV-2 infection. This prospective study involved 674 patients with suspected SARS-CoV-2 infection. Paired nasopharyngeal and saliva samples were processed by RT-qPCR. Sensitivity, specificity, and kappa coefficient were used to evaluate the results from both samples. We considered the influence of age, symptoms, chronic conditions, and sample processing with lysis buffer. Of the 674 patients, 636 (94.4%) had valid results from both samples. The virus detection in saliva compared to a nasopharyngeal sample (gold standard) was 51.9% (95% CI: 46.3%–57.4%) and increased to 91.6% (95% CI: 86.7%–96.5%) when the cycle threshold (Ct) was ≤ 30. The specificity of the saliva sample was 99.1% (95% CI: 97.0%–99.8%). The concordance between samples was 75% (κ = 0.50; 95% CI: 0.45–0.56). The Ct values were significantly higher in saliva. In conclusion, saliva sample utility is limited for clinical diagnosis, but could be a useful alternative for the detection of SARS-CoV-2 in massive screening studies, when the availability of trained professionals for sampling or personal protection equipment is limited.

scholarly journals Patterns of Macular Edema in Uveitis as Diagnosed by Optical Coherence Tomography in Tertiary Eye Center

2018 ◽  
Vol 10 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Sharad Gupta ◽  
Dev Narayan Shah ◽  
Sagun Narayan Joshi ◽  
Manoj Aryal ◽  
Lila Raj Puri
Keyword(s):  
Optical Coherence Tomography ◽  
Macular Edema ◽  
Serous Retinal Detachment ◽  
Posterior Uveitis ◽  
Invasive Technique ◽  
Optical Coherence ◽  
Fundus Fluorescein Angiography ◽  
Cross Sectional ◽  
Female Ratio ◽  
Uveitic Macular Edema

Aim: The aim of the study is to classify the patterns of uveitic macular edema using Optical Coherence Tomography as a diagnostic tool.Methodology: It is the Descriptive, cross-sectional study. All patients fulfilling the diagnostic criteria with Optical coherence tomography diagnosed macular edema were enrolled from 1 January 2012 to 30 June 2013. Patterns of uveitic macular edema were classified.Results: A total of 65 eyes of 47 patients were included. Twenty eight (59.57%) were male. The male to female ratio was 1.5:1. The mean age was 38 years (SD 14.68). Twenty nine patients (61.71%) had unilateral involvement and 18 (38.29%) had bilateral involvement. Forty five eyes of 33 cases (69.23%, 70.21%) had intermediate uveitis, followed by 10 eyes of 7 cases (15.38, 14.9%) of posterior uveitis, 6 eyes of 5 cases (9.23%, 10.63 %) of anterior uveitis and 4 eyes of 2 cases (6.16%,4.2%) of pan-uveitis. Patterns of macular edema were classified: diff use macular edema (DME), cystoid macular edema (CME) and serous retinal detachment (SRD) of which 35 (53.84%) eyes had CME. The etiological diagnosis was found in 7(14.90 %) out of 47 patients.Conclusion: A significant percentage of cases were idiopathic. Macular edema may go unnoticed unless OCT is performed. Macular detachment is an important feature of macular edema that affects visual acuity and is not readily detected by Fundus Fluorescein Angiography (FFA). Optical coherence tomography (OCT) is safe and non-invasive technique and has the potential for measuring changes in retinal thickness and axial extent of edema.

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