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.

scholarly journals An approach to automated retinal layer segmentation in SDOCT images

2018 ◽  
Vol 7 (2.25) ◽  
pp. 56
Author(s):  
Mohandass G ◽  
Hari Krishnan G ◽  
Hemalatha R J
Keyword(s):  
Optical Coherence Tomography ◽  
Imaging Technique ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Seed Point ◽  
Deformable Contour ◽  
Gradient Based ◽  
Oct Imaging ◽  
Layer Segmentation ◽  
Region Growth

The optical coherence tomography (OCT) imaging technique is a precise and well-known approach to the diagnosis of retinal layers. The pathological changes in the retina challenge the accuracy of computational segmentation approaches in the evaluation and identification of defects in the boundary layer. The layer segmentations and boundary detections are distorted by noise in the computation. In this work, we propose a fully automated segmentation algorithm using a denoising technique called the Boisterous Obscure Ratio (BOR) for human and mammal retina. First, the BOR is derived using noise detection, i.e., from the Robust Outlyingness Ratio (ROR). It is then applied to edge and layer detection using a gradient-based deformable contour model. Second, the image is vectorised. In this method, a cluster and column intensity grid is applied to identify and determine the unsegmented layers. Using the layer intensity and a region growth seed point algorithm, segmentation of the prominent layers is achieved. The automatic BOR method is an image segmentation process that determines the eight layers in retinal spectral domain optical coherence tomography images. The highlight of the BOR method is that the results produced are accurate, highly substantial, and effective, although time consuming. 

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 Cellular-Scale Imaging of Transparent Retinal Structures and Processes Using Adaptive Optics Optical Coherence Tomography

2020 ◽  
Vol 6 (1) ◽  
pp. 115-148 ◽  
Author(s):  
Donald T. Miller ◽  
Kazuhiro Kurokawa
Keyword(s):  
Optical Coherence Tomography ◽  
High Resolution ◽  
Adaptive Optics ◽  
State Of The Art ◽  
Disease Onset ◽  
Time Lapse ◽  
Optical Coherence ◽  
Physiological Processes ◽  
Resolution Imaging ◽  
Human Eyes

High-resolution retinal imaging is revolutionizing how scientists and clinicians study the retina on the cellular scale. Its exquisite sensitivity enables time-lapse optical biopsies that capture minute changes in the structure and physiological processes of cells in the living eye. This information is increasingly used to detect disease onset and monitor disease progression during early stages, raising the possibility of personalized eye care. Powerful high-resolution imaging tools have been in development for more than two decades; one that has garnered considerable interest in recent years is optical coherence tomography enhanced with adaptive optics. State-of-the-art adaptive optics optical coherence tomography (AO-OCT) makes it possible to visualize even highly transparent cells and measure some of their internal processes at all depths within the retina, permitting reconstruction of a 3D view of the living microscopic retina. In this review, we report current AO-OCT performance and its success in visualizing and quantifying these once-invisible cells in human eyes.

scholarly journals Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images

2009 ◽  
Vol 28 (9) ◽  
pp. 1436-1447 ◽  
Author(s):  
M.K. Garvin ◽  
M.D. Abramoff ◽  
Xiaodong Wu ◽  
S.R. Russell ◽  
T.L. Burns ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Layer Segmentation
Sign in / Sign up

Export Citation Format

Share Document