用AI精准分割视网膜锥细胞，助力眼病诊断
Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging
- 结合共焦与非共焦成像，用U-Net模型分割锥细胞
- 在双模态图像上实现一致分割结果，提升准确性
- 适合眼科临床研究与视网膜疾病评估使用
视网膜锥细胞的准确检测与分割对眼病诊断至关重要。本研究利用自适应光学扫描光检眼镜（AOSLO）获取的共焦与非共焦分裂探测图像，分析光感受器以提高精度。精确分割有助于理解每个锥细胞的形状、面积及分布，进而估算周围视杆细胞占据区域，从而计算感兴趣区内的锥细胞密度。密度是评估视网膜健康与功能的关键指标。我们采用基于U-Net的两种分割模型：StarDist用于共焦模态，Cellpose用于计算模态。在双模态图像上实现一致分割结果，验证了方法的可靠性与潜在临床应用价值。
原文摘要 · Abstract (English)
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.
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