scholarly journals Automatic Segmentation of Macular Edema in Retinal OCT Images Using Improved U-Net++

2020 ◽  
Vol 10 (16) ◽  
pp. 5701
Author(s):  
Zhijun Gao ◽  
Xingle Wang ◽  
Yi Li
Keyword(s):  
Optical Coherence Tomography ◽  
Macular Edema ◽  
Automatic Segmentation ◽  
Semantic Gap ◽  
Segmentation Method ◽  
Feature Maps ◽  
The Public ◽  
Duke University ◽  
Public Dataset ◽  
Oct Image

The number and volume of retinal macular edemas are important indicators for screening and diagnosing retinopathy. Aiming at the problem that the segmentation method of macular edemas in a retinal optical coherence tomography (OCT) image is not ideal in segmentation of diverse edemas, this paper proposes a new method of automatic segmentation of macular edema regions in retinal OCT images using the improved U-Net++. The proposed method makes full use of the U-Net++ re-designed skip pathways and dense convolution block; reduces the semantic gap of the feature maps in the encoder/decoder sub-network; and adds the improved Resnet network as the backbone, which make the extraction of features in the edema regions more accurate and improves the segmentation effect. The proposed method was trained and validated on the public dataset of Duke University, and the experiments demonstrated the proposed method can not only improve the overall segmentation effect, but also can significantly improve the segmented precision for diverse edema in multi-regions, as well as reducing the error of the number of edema regions.

scholarly journals Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI

2020 ◽  
Vol 10 (7) ◽  
pp. 2601 ◽  
Author(s):  
Indriani P. Astono ◽  
James S. Welsh ◽  
Stephan Chalup ◽  
Peter Greer
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Feature Maps ◽  
The Public ◽  
Prostate Segmentation ◽  
Segmentation Approach ◽  
Model Components ◽  
The Individual ◽  
Deep Learning Model ◽  
Better Than

In this paper, we develop an optimised state-of-the-art 2D U-Net model by studying the effects of the individual deep learning model components in performing prostate segmentation. We found that for upsampling, the combination of interpolation and convolution is better than the use of transposed convolution. For combining feature maps in each convolution block, it is only beneficial if a skip connection with concatenation is used. With respect to pooling, average pooling is better than strided-convolution, max, RMS or L2 pooling. Introducing a batch normalisation layer before the activation layer gives further performance improvement. The optimisation is based on a private dataset as it has a fixed 2D resolution and voxel size for every image which mitigates the need of a resizing operation in the data preparation process. Non-enhancing data preprocessing was applied and five-fold cross-validation was used to evaluate the fully automatic segmentation approach. We show it outperforms the traditional methods that were previously applied on the private dataset, as well as outperforming other comparable state-of-the-art 2D models on the public dataset PROMISE12.

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.

scholarly journals Development of a Semi-Automatic Segmentation Method for Retinal OCT Images Tested in Patients with Diabetic Macular Edema

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82922 ◽  
Author(s):  
Yijun Huang ◽  
Ronald P. Danis ◽  
Jeong W. Pak ◽  
Shiyu Luo ◽  
James White ◽  
...  
Keyword(s):  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Automatic Segmentation ◽  
Segmentation Method

scholarly journals 3D automatic segmentation method for retinal optical coherence tomography volume data using boundary surface enhancement

2016 ◽  
Vol 09 (02) ◽  
pp. 1650008 ◽  
Author(s):  
Yankui Sun ◽  
Tian Zhang ◽  
Yue Zhao ◽  
Yufan He
Keyword(s):  
Optical Coherence Tomography ◽  
Automatic Segmentation ◽  
Boundary Surface ◽  
Optical Coherence ◽  
Volume Data ◽  
Segmentation Method ◽  
Layer Boundary ◽  
Position Information ◽  
Pixel Intensity ◽  
Boundary Surfaces

With the introduction of spectral-domain optical coherence tomography (SD-OCT), 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 three-dimensional (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 datasets contains 97 OCT B-Scan images with a resolution of [Formula: see text] (each B-Scan comprising 512 A-Scans containing 496 pixels); experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.

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.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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.

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