Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula
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Maculopathy is the group of diseases that affects central vision of a person and they are often associated with diabetes. Many researchers reported automated diagnosis of maculopathy from optical coherence tomography (OCT) images. However, to the best of our knowledge there is no literature that presents a complete 3D suite for the extraction as well as diagnosis of macula. Therefore, this paper presents a multilayered convolutional neural networks (CNN) structure tensor Delaunay triangulation and morphing based fully autonomous system that extracts up to nine retinal and choroidal layers along with the macular fluids. Furthermore, the proposed system utilizes the extracted retinal information for the automated diagnosis of maculopathy as well as for the robust reconstruction of 3D macula of retina. The proposed system has been validated on 41,921 retinal OCT scans acquired from different OCT machines and it significantly outperformed existing state of the art solutions by achieving the mean accuracy of 95.27% for extracting retinal and choroidal layers, mean dice coefficient of 0.90 for extracting fluid pathology and the overall accuracy of 96.07% for maculopathy diagnosis. To the best of our knowledge, the proposed framework is first of its kind that provides a fully automated and complete 3D integrated solution for the extraction of candidate macula along with its fully automated diagnosis against different macular syndromes.
KeywordsOptical coherence tomography Image processing Ophthalmology Pattern recognition Neural networks
We are thankful to AFIO, Rawalpindi and Amanat Eye Hospital, Rawalpindi for providing us the dataset. We are also thankful to Vision and Image Processing Lab, Duke University for making their OCT datasets publicly available.
This work has been funded by Ignite National Technology Fund, Ministry of Information Technology, Government of Pakistan.
Compliance with Ethical Standards
Conflict of Interest
All authors declare that they don’t have any conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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