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Optical Coherence Tomography Imaging Analysis for Retinal Disease
Optical coherence tomography imaging analysis for retinal disease diagnosis and detection, including AMD, diabetic retinopathy, and glaucoma, with high accuracy rates and potential for early prevention.
- The number of people suffering from AMD and diabetic retinopathy has increased significantly in the last 10 years.
- Optical coherence tomography (OCT) is a better technology for understanding retinal diseases, with an accuracy of nearly 97%.
- The technique used is a simple similar annealing method to estimate parameters.
- UNET is a popular segmentation network used in the field.
- The Aurora method was also applied with similar results.
- The nodes in the graph are the end pixels of each group, and the edges are defined by the number of pixels in each group.
- The internal limiting membrane is the surface of the retina, which separates it from the choroid.
- The optic disc is a critical area that can indicate glaucoma.
- The OPL and OLM boundaries were extracted using computer programming.
- The drusen are white spots that bulge out the retina.
- Micro aneurysm is a blood clot that leaks blood in the retina.
- The Bruce membrane is a yellow part of the retina that is an important biomarker for eye health.
- There is no existing automated tool to separate the 10 layers of the retina, but the OCT Inspector tool can do so.
- The system is based on intensity-based algorithms with various approaches.
- The data set used is based on medical images, and the manual segmentation was done by ophthalmologists.
- The 10 layers of the retina were identified and segmented using the system.
- The system uses a fully connected graph to define distances between pixels.
- The edge weights are defined by the number of pixels in each group.
- The shortest path objective can be blended into the learning process.
- The system is capable of detecting various retinal pathologies, including drusen, micro aneurysm, and geographic atrophy.
- The OCT Inspector tool has a high accuracy rate, even with manual segmentation.
- The system can detect diseases such as glaucoma, diabetic retinopathy, and AMD.
- The system can also detect non(ret) eye related diseases, such as hypertension and heart disease.
- The system uses a combination of machine learning and computer programming to identify the layers of the retina.
- The eventual goal is to use the system for early detection and prevention of retinal diseases.