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 (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/141547 |
| _version_ | 1826129686900506624 |
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| author | Quek, Kenny Jun Hao |
| author2 | Domenico Campolo |
| author_facet | Domenico Campolo Quek, Kenny Jun Hao |
| author_sort | Quek, Kenny Jun Hao |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:44:36Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/141547 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:44:36Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1415472023-03-04T19:44:59Z Automated artefacts detection for OCT-Angiography images using deep learning Quek, Kenny Jun Hao Domenico Campolo School of Mechanical and Aerospace Engineering SERI d.campolo@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Mechanical engineering 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. Bachelor of Engineering (Mechanical Engineering) 2020-06-09T03:56:59Z 2020-06-09T03:56:59Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/141547 en B057 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Mechanical engineering Quek, Kenny Jun Hao Automated artefacts detection for OCT-Angiography images using deep learning |
| title | Automated artefacts detection for OCT-Angiography images using deep learning |
| title_full | Automated artefacts detection for OCT-Angiography images using deep learning |
| title_fullStr | Automated artefacts detection for OCT-Angiography images using deep learning |
| title_full_unstemmed | Automated artefacts detection for OCT-Angiography images using deep learning |
| title_short | Automated artefacts detection for OCT-Angiography images using deep learning |
| title_sort | automated artefacts detection for oct angiography images using deep learning |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Mechanical engineering |
| url | https://hdl.handle.net/10356/141547 |
| work_keys_str_mv | AT quekkennyjunhao automatedartefactsdetectionforoctangiographyimagesusingdeeplearning |