Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912078574387200 |
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| author | Shrestha, Prajol Kulyabin, Mikhail Sindel, Aline Pedersen, Hilde R. Gilson, Stuart Baraas, Rigmor Maier, Andreas |
| author_facet | Shrestha, Prajol Kulyabin, Mikhail Sindel, Aline Pedersen, Hilde R. Gilson, Stuart Baraas, Rigmor Maier, Andreas |
| contents | Accurate detection and segmentation of cone cells in the retina are essential for diagnosing and managing retinal diseases. In this study, we used advanced imaging techniques, including confocal and non-confocal split detector images from adaptive optics scanning light ophthalmoscopy (AOSLO), to analyze photoreceptors for improved accuracy. Precise segmentation is crucial for understanding each cone cell's shape, area, and distribution. It helps to estimate the surrounding areas occupied by rods, which allows the calculation of the density of cone photoreceptors in the area of interest. In turn, density is critical for evaluating overall retinal health and functionality. We explored two U-Net-based segmentation models: StarDist for confocal and Cellpose for calculated modalities. Analyzing cone cells in images from two modalities and achieving consistent results demonstrates the study's reliability and potential for clinical application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15158 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging Shrestha, Prajol Kulyabin, Mikhail Sindel, Aline Pedersen, Hilde R. Gilson, Stuart Baraas, Rigmor Maier, Andreas Image and Video Processing Computer Vision and Pattern Recognition Accurate detection and segmentation of cone cells in the retina are essential for diagnosing and managing retinal diseases. In this study, we used advanced imaging techniques, including confocal and non-confocal split detector images from adaptive optics scanning light ophthalmoscopy (AOSLO), to analyze photoreceptors for improved accuracy. Precise segmentation is crucial for understanding each cone cell's shape, area, and distribution. It helps to estimate the surrounding areas occupied by rods, which allows the calculation of the density of cone photoreceptors in the area of interest. In turn, density is critical for evaluating overall retinal health and functionality. We explored two U-Net-based segmentation models: StarDist for confocal and Cellpose for calculated modalities. Analyzing cone cells in images from two modalities and achieving consistent results demonstrates the study's reliability and potential for clinical application. |
| title | Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.15158 |