scholarly journals ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Tin Tin Khaing ◽  
Takayuki Okamoto ◽  
Chen Ye ◽  
Md. Abdul Mannan ◽  
Hirotaka Yokouchi ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Vessel Segmentation ◽  
Optical Coherence

scholarly journals Novel Application of Long Short-Term Memory Network for 3D to 2D Retinal Vessel Segmentation in Adaptive Optics—Optical Coherence Tomography Volumes

2021 ◽  
Vol 11 (20) ◽  
pp. 9475
Author(s):  
Christopher T. Le ◽  
Dongyi Wang ◽  
Ricardo Villanueva ◽  
Zhuolin Liu ◽  
Daniel X. Hammer ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Blood Vessel ◽  
Adaptive Optics ◽  
Short Term Memory ◽  
Vessel Segmentation ◽  
Invasive Technique ◽  
Optical Coherence ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Adaptive optics—optical coherence tomography (AO-OCT) is a non-invasive technique for imaging retinal vascular and structural features at cellular-level resolution. Whereas retinal blood vessel density is an important biomarker for ocular diseases, particularly glaucoma, automated blood vessel segmentation tools in AO-OCT have not yet been explored. One reason for this is that AO-OCT allows for variable input axial dimensions, which are not well accommodated by 2D-2D or 3D-3D segmentation tools. We propose a novel bidirectional long short-term memory (LSTM)-based network for 3D-2D segmentation of blood vessels within AO-OCT volumes. This technique incorporates inter-slice connectivity and allows for variable input slice numbers. We compare this proposed model to a standard 2D UNet segmentation network considering only volume projections. Furthermore, we expanded the proposed LSTM-based network with an additional UNet to evaluate how it refines network performance. We trained, validated, and tested these architectures in 177 AO-OCT volumes collected from 18 control and glaucoma subjects. The LSTM-UNet has statistically significant improvement (p < 0.05) in AUC (0.88) and recall (0.80) compared to UNet alone (0.83 and 0.70, respectively). LSTM-based approaches had longer evaluation times than the UNet alone. This study shows that a bidirectional convolutional LSTM module improves standard automated vessel segmentation in AO-OCT volumes, although with higher time cost.

scholarly journals Association Between the Severity of Diabetic Retinopathy and Optical Coherence Tomography Angiography Metrics

2021 ◽  
Vol 12 ◽  
Author(s):  
Binxin Xu ◽  
Jiahui Chen ◽  
Shaohua Zhang ◽  
Shengli Shen ◽  
Xuan Lan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Systemic Risk ◽  
Optical Coherence Tomography Angiography ◽  
Vessel Segmentation ◽  
Optical Coherence ◽  
Segmentation Method ◽  
The Relationship ◽  
Systemic Risk Factors

Diabetic retinopathy, the most serious ocular complication of diabetes, imposes a serious economic burden on society. Automatic and objective assessment of vessel changes can effectively manage diabetic retinopathy and prevent blindness. Optical coherence tomography angiography (OCTA) metrics have been confirmed to be used to assess vessel changes. The accuracy and reliability of OCTA metrics are restricted by vessel segmentation methods. In this study, a multi-branch retinal vessel segmentation method is proposed, which is comparable to the segmentation results obtained from the manual segmentation, effectively extracting vessels in low contrast areas and improving the integrity of the extracted vessels. OCTA metrics based on the proposed segmentation method were validated to be reliable for further analysis of the relationship between OCTA metrics and diabetes and the severity of diabetic retinopathy. Changes in vessel morphology are influenced by systemic risk factors. However, there is a lack of analysis of the relationship between OCTA metrics and systemic risk factors. We conducted a cross-sectional study that included 362 eyes of 221 diabetic patients and 1,151 eyes of 587 healthy people. Eight systemic risk factors were confirmed to be closely related to diabetes. After controlling these systemic risk factors, significant OCTA metrics (such as vessel complexity index, vessel diameter index, and mean thickness of retinal nerve fiber layer centered in the macular) were found to be related to diabetic retinopathy and severe diabetic retinopathy. This study provides evidence to support the potential value of OCTA metrics as biomarkers of diabetic retinopathy.

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