Shrestha P, Kulyabin M, Sindel A, Baraas R, Gilson S, Pedersen H, Maier A (2025)
Publication Type: Conference contribution
Publication year: 2025
Publisher: Springer Science and Business Media Deutschland GmbH
Pages Range: 254-259
Conference Proceedings Title: Informatik aktuell
ISBN: 9783658474218
DOI: 10.1007/978-3-658-47422-5_55
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 non-confocal modalities. Analyzing cone cells in images from two modalities and achieving consistent results demonstrates the study’s reliability and potential for clinical application.
APA:
Shrestha, P., Kulyabin, M., Sindel, A., Baraas, R., Gilson, S., Pedersen, H., & Maier, A. (2025). Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging. In Christoph Palm, Katharina Breininger, Thomas Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Thomas M. Tolxdorff (Eds.), Informatik aktuell (pp. 254-259). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
MLA:
Shrestha, Prajol, et al. "Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging." Proceedings of the German Conference on Medical Image Computing, 2025, Regensburg Ed. Christoph Palm, Katharina Breininger, Thomas Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Thomas M. Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2025. 254-259.
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