Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2020
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| _version_ | 1866911504674062336 |
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| author | Kepp, Timo Sudkamp, Helge von der Burchard, Claus Schenke, Hendrik Koch, Peter Hüttmann, Gereon Roider, Johann Heinrich, Mattias P. Handels, Heinz |
| author_facet | Kepp, Timo Sudkamp, Helge von der Burchard, Claus Schenke, Hendrik Koch, Peter Hüttmann, Gereon Roider, Johann Heinrich, Mattias P. Handels, Heinz |
| contents | The treatment of age-related macular degeneration (AMD) requires continuous eye exams using optical coherence tomography (OCT). The need for treatment is determined by the presence or change of disease-specific OCT-based biomarkers. Therefore, the monitoring frequency has a significant influence on the success of AMD therapy. However, the monitoring frequency of current treatment schemes is not individually adapted to the patient and therefore often insufficient. While a higher monitoring frequency would have a positive effect on the success of treatment, in practice it can only be achieved with a home monitoring solution. One of the key requirements of a home monitoring OCT system is a computer-aided diagnosis to automatically detect and quantify pathological changes using specific OCT-based biomarkers. In this paper, for the first time, retinal scans of a novel self-examination low-cost full-field OCT (SELF-OCT) are segmented using a deep learning-based approach. A convolutional neural network (CNN) is utilized to segment the total retina as well as pigment epithelial detachments (PED). It is shown that the CNN-based approach can segment the retina with high accuracy, whereas the segmentation of the PED proves to be challenging. In addition, a convolutional denoising autoencoder (CDAE) refines the CNN prediction, which has previously learned retinal shape information. It is shown that the CDAE refinement can correct segmentation errors caused by artifacts in the OCT image. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2001_08480 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning Kepp, Timo Sudkamp, Helge von der Burchard, Claus Schenke, Hendrik Koch, Peter Hüttmann, Gereon Roider, Johann Heinrich, Mattias P. Handels, Heinz Image and Video Processing Computer Vision and Pattern Recognition The treatment of age-related macular degeneration (AMD) requires continuous eye exams using optical coherence tomography (OCT). The need for treatment is determined by the presence or change of disease-specific OCT-based biomarkers. Therefore, the monitoring frequency has a significant influence on the success of AMD therapy. However, the monitoring frequency of current treatment schemes is not individually adapted to the patient and therefore often insufficient. While a higher monitoring frequency would have a positive effect on the success of treatment, in practice it can only be achieved with a home monitoring solution. One of the key requirements of a home monitoring OCT system is a computer-aided diagnosis to automatically detect and quantify pathological changes using specific OCT-based biomarkers. In this paper, for the first time, retinal scans of a novel self-examination low-cost full-field OCT (SELF-OCT) are segmented using a deep learning-based approach. A convolutional neural network (CNN) is utilized to segment the total retina as well as pigment epithelial detachments (PED). It is shown that the CNN-based approach can segment the retina with high accuracy, whereas the segmentation of the PED proves to be challenging. In addition, a convolutional denoising autoencoder (CDAE) refines the CNN prediction, which has previously learned retinal shape information. It is shown that the CDAE refinement can correct segmentation errors caused by artifacts in the OCT image. |
| title | Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2001.08480 |