Real-time retinal layer segmentation of OCT images: from graph cut to deep learning (Conference Presentation)

2020 ◽  
Author(s):  
Svetlana Borkovkina ◽  
Worawee Janpongsri ◽  
Acner Camino Benech ◽  
Marinko V. Sarunic ◽  
Yifan Jian
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Graph Cut ◽  
Retinal Layer ◽  
Layer Segmentation

scholarly journals Automated Retinal Layer Segmentation Using Graph-based Algorithm Incorporating Deep-learning-derived Information

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zubin Mishra ◽  
Anushika Ganegoda ◽  
Jane Selicha ◽  
Ziyuan Wang ◽  
SriniVas R. Sadda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Retinal Layer ◽  
Layer Segmentation

scholarly journals Automatic choroidal segmentation in OCT images using supervised deep learning methods

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jason Kugelman ◽  
David Alonso-Caneiro ◽  
Scott A. Read ◽  
Jared Hamwood ◽  
Stephen J. Vincent ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Retinal Layer ◽  
Learning Methods ◽  
Cross Sectional ◽  
Physiological Processes ◽  
Analysis Technique ◽  
Layer Segmentation

Abstract The analysis of the choroid in the eye is crucial for our understanding of a range of ocular diseases and physiological processes. Optical coherence tomography (OCT) imaging provides the ability to capture highly detailed cross-sectional images of the choroid yet only a very limited number of commercial OCT instruments provide methods for automatic segmentation of choroidal tissue. Manual annotation of the choroidal boundaries is often performed but this is impractical due to the lengthy time taken to analyse large volumes of images. Therefore, there is a pressing need for reliable and accurate methods to automatically segment choroidal tissue boundaries in OCT images. In this work, a variety of patch-based and fully-convolutional deep learning methods are proposed to accurately determine the location of the choroidal boundaries of interest. The effect of network architecture, patch-size and contrast enhancement methods was tested to better understand the optimal architecture and approach to maximize performance. The results are compared with manual boundary segmentation used as a ground-truth, as well as with a standard image analysis technique. Results of total retinal layer segmentation are also presented for comparison purposes. The findings presented here demonstrate the benefit of deep learning methods for segmentation of the chorio-retinal boundary analysis in OCT images.

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

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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