scholarly journals Real-time retinal layer segmentation of OCT volumes with GPU accelerated inferencing using a compressed, low-latency neural network

2020 ◽  
Vol 11 (7) ◽  
pp. 3968
Author(s):  
Svetlana Borkovkina ◽  
Acner Camino ◽  
Worawee Janpongsri ◽  
Marinko V. Sarunic ◽  
Yifan Jian
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Low Latency ◽  
Retinal Layer ◽  
Layer Segmentation

scholarly journals Real-time Fully Automated Internal Layer Segmentation of Human Retina in Optical Coherence Tomography Images

2020 ◽  
Vol 9 (3) ◽  
pp. 1951-1957
Keyword(s):  
Optical Coherence Tomography ◽  
Real Time ◽  
Pigment Epithelium ◽  
Retinal Pigment ◽  
Intensity Variation ◽  
Frame Rate ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Retinal Layers ◽  
Layer Segmentation

In the field of ophthalmology, optical coherence tomography (OCT) has proven to be a powerful imaging technique when it comes to diagnosing various eye-related diseases. This research article introduces a real-time automatic retinal layer segmentation algorithm based on intensity variation in the OCT images. The built algorithm is capable of detecting internal retinal layers like the internal limiting membrane (ILM), the retinal pigment epithelium (RPE) and the retinal nerve fiber layer (RNFL) with micrometer level precision, the algorithm uses openMP for parallelized computation for real-time visualization of the segmented retinal layers. The total execution time of the algorithm was evaluated using various image sizes and compared with the OCT frame rate to demonstrate the efficiency of real-time segmentation.

scholarly journals Pseudo‐real‐time retinal layer segmentation for high‐resolution adaptive optics optical coherence tomography

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Worawee Janpongsri ◽  
Joey Huang ◽  
Ringo Ng ◽  
Daniel J. Wahl ◽  
Marinko V. Sarunic ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
High Resolution ◽  
Real Time ◽  
Adaptive Optics ◽  
Retinal Layer ◽  
Optical Coherence ◽  
Layer Segmentation

Small Scale and Low Latency Oriented Neural Network Physical Unclonable Function and its Evaluation

2020 ◽  
Vol 140 (12) ◽  
pp. 1297-1306
Author(s):  
Shu Takemoto ◽  
Kazuya Shibagaki ◽  
Yusuke Nozaki ◽  
Masaya Yoshikawa
Keyword(s):  
Neural Network ◽  
Small Scale ◽  
Low Latency ◽  
Physical Unclonable Function
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