Cunefare David, Huckenpahler Alison L, Patterson Emily J, Dubra Alfredo, Carroll Joseph, Farsiu Sina
Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Department of Cell Biology, Neurobiology, & Anatomy, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Biomed Opt Express. 2019 Jul 8;10(8):3815-3832. doi: 10.1364/BOE.10.003815. eCollection 2019 Aug 1.
Quantification of the human rod and cone photoreceptor mosaic in adaptive optics scanning light ophthalmoscope (AOSLO) images is useful for the study of various retinal pathologies. Subjective and time-consuming manual grading has remained the gold standard for evaluating these images, with no well validated automatic methods for detecting individual rods having been developed. We present a novel deep learning based automatic method, called the rod and cone CNN (RAC-CNN), for detecting and classifying rods and cones in multimodal AOSLO images. We test our method on images from healthy subjects as well as subjects with achromatopsia over a range of retinal eccentricities. We show that our method is on par with human grading for detecting rods and cones.
在自适应光学扫描激光检眼镜（AOSLO）图像中对人眼视杆和视锥光感受器镶嵌结构进行量化，有助于研究各种视网膜病变。主观且耗时的人工分级一直是评估这些图像的金标准，目前尚未开发出经过充分验证的用于检测单个视杆的自动方法。我们提出了一种基于深度学习的新颖自动方法，称为视杆和视锥卷积神经网络（RAC-CNN），用于在多模态AOSLO图像中检测和分类视杆和视锥。我们在一系列视网膜偏心度下，对来自健康受试者以及患有全色盲受试者的图像测试了我们的方法。我们表明，在检测视杆和视锥方面，我们的方法与人工分级相当。
