scholarly journals Lymphatic vessel segmentation in optical coherence tomography by adding U-Net-based CNN for artifact minimization

2020 ◽  
Vol 11 (5) ◽  
pp. 2679
Author(s):  
Pei-Yu Lai ◽  
Chung-Hsing Chang ◽  
Hong-Ren Su ◽  
Wen-Chuan Kuo
Keyword(s):  
Optical Coherence Tomography ◽  
Lymphatic Vessel ◽  
Vessel Segmentation ◽  
Optical Coherence

scholarly journals In Vivo Molecular Optical Coherence Tomography of Lymphatic Vessel Endothelial Hyaluronan Receptors

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Peng Si ◽  
Debasish Sen ◽  
Rebecca Dutta ◽  
Siavash Yousefi ◽  
Roopa Dalal ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Lymphatic Vessel ◽  
Optical Coherence ◽  
Hyaluronan Receptors

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.

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