Automated artefacts detection for OCT-Angiography images using deep learning
Optical Coherence Tomography Angiography (OCT-Angiography) is a recent non-invasive imaging technique which enables visualization of microvasculature in the eye. There is increasing interest in the use of OCT-Angiography for disease studies and diagnosis. However, interpretation of OCT-Angiography c...
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Format: | Final Year Project |
Language: | English |
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Nanyang Technological University
2020
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Online Access: | https://hdl.handle.net/10356/141547 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Optical Coherence Tomography Angiography (OCT-Angiography) is a recent non-invasive imaging technique which enables visualization of microvasculature in the eye. There is increasing interest in the use of OCT-Angiography for disease studies and diagnosis. However, interpretation of OCT-Angiography can be affected by localized artefacts which only degrades image quality in a focal region of the image. This study presents a Defect Detection System (DDS), capable of automatic identification of artefacts in an OCT-Angiography image. Three convolutional neural network (CNN) architectures (VGG-16, VGG-19, ResNet-50) from the ImageNet classification were used to train the automated classifier using transfer learning. Results show that VGG-19 obtained the highest accuracy of 99.52% compared to the other networks. The results are promising for the use of DDS for automated OCT-Angiography image quality assessment. |
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