Author Affiliations
Abstract
1 Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
2 Department of Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230041, P. R. China
3 Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, P. R. China
Cone photoreceptor cell identification is important for the early diagnosis of retinopathy. In this study, an object detection algorithm is used for cone cell identification in confocal adaptive optics scanning laser ophthalmoscope (AOSLO) images. An effectiveness evaluation of identification using the proposed method reveals precision, recall, and F1-score of 95.8%, 96.5%, and 96.1%, respectively, considering manual identification as the ground truth. Various object detection and identification results from images with different cone photoreceptor cell distributions further demonstrate the performance of the proposed method. Overall, the proposed method can accurately identify cone photoreceptor cells on confocal adaptive optics scanning laser ophthalmoscope images, being comparable to manual identification.
Biomedical image processing retinal imaging adaptive optics scanning laser ophthalmoscope object detection. Journal of Innovative Optical Health Sciences
2022, 15(1): 2250001
Author Affiliations
Abstract
1 Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
2 Department of Biomedical Engineering, University of Science and Technology of China, Hefei 230041, China
3 Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
An automated superpixels identification/mosaicking method is presented for the analysis of cone photoreceptor cells with the use of adaptive optics scanning laser ophthalmoscope (AO-SLO) images. This is an image oversegmentation method used for the identification and mosaicking of cone photoreceptor cells in AO-SLO images. It includes image denoising, estimation of the cone photoreceptor cell number, superpixels segmentation, merging of superpixels, and final identification and mosaicking processing steps. The effectiveness of the presented method was confirmed based on its comparison with a manual method in terms of precision, recall, and F1-score of 77.3%, 95.2%, and 85.3%, respectively.
biomedical optics retinal imaging adaptive optics scanning laser ophthalmoscope cone photoreceptor cell superpixels Chinese Optics Letters
2020, 18(10): 101701
1 中国科学院自适应光学重点实验室，四川成都 610209
2 中国科学院光电技术研究所，四川成都 610209
3 中国科学院大学，北京 100049
自适应共焦检眼镜以其高分辨率、动态成像等光学特性，已经在生物医学和临床医学的多个领域得到了广泛而具体的应用。为了能够将非圆形光瞳滤波器等瞳面调制技术运用于其中，并不对波前探测产生影响，系统需要利用两个光源分别进行成像和波前校正。本文首先设计了一套基于双光源的自适应共焦检眼镜，对不同光源的人眼像差进行测量，分析了其主要差异。然后对双光源系统的像差校正能力和高分辨成像能力进行了验证，系统闭环后的图像的亮度、对比度和分辨率都有了显著的提高。最后验证了使用半圆形光瞳实现暗场成像的可行性，并得到了模拟人眼的明暗场图像。
自适应光学 共焦检眼镜 双光源 adaptive optics confocal laser ophthalmoscope two sources
1 中国科学院光电技术研究所自适应光学重点实验室, 四川 成都 610209
2 中国科学院研究生院, 北京 100049
高速线扫描共焦检眼镜使用线光束照明眼底视网膜，同时利用线阵CCD对视网膜平面的单次散射线光束探测成像。系统光学放大率为7倍，横向分辨率小于10 μm，对于58 kHz线频的1024 pixel×512 pixel成像模式，成像帧频高达110 frame/s。该系统实现了高分辨率、高帧频模拟人眼实验图像的获取。
成像系统 共焦 线扫描 检眼镜 视网膜 高帧频成像
Author Affiliations
Abstract
1 Department of Biomedical Engineering The Catholic University of America 620 Michigan Ave., N.E., Washington, DC 20064, USA
2 ECE Department, Portland State University 1900 SW Fourth Avenue, Portland, OR 97201, USA
3 Polaris Sensor Technologies, 200 Westside Square Suite 320 Huntsville, AL 35801, USA
4 Wilmer Eye Institute, Johns Hopkins University Baltimore, MD 21287, USA
Measurement of both oxygen saturation and blood flow in the retinal vessels has proved to give important information about the eye health and the onset of eye pathologies such as diabetic retinopathy. In this study, we present the implementation, on a commercially available fundus camera, of a retinal imager and a retina blood flow velocimeter. The retinal imager uses division of aperture to acquire nine wavelength-dependent sub-images of the retina. Careful consideration is taken to improve image transfer by measuring the optical properties of the fundus camera and modeling the optical train in Zemax. This part of the setup is calibrated with optical phantoms of known optical properties that are also used to build a lookup table (LUT) linking phantom optical properties to measured reflectance. The retina blood flow velocimeter relies on tracking clusters of erythrocytes and uses a fast acquisition camera attached to a zoom lens, with a green illumination LED-engine. Calibration is provided using a calibrated quartz capillary tube and human blood at a known flow rate. Optical properties of liquid phantoms are retrieved from measured reflectance using the LUT, and blood flow measurements in the retina are presented.
Retinal oximetry fundus ophthalmoscope multi-aperture camera blood flow velocity diabetic retinopathy Journal of Innovative Optical Health Sciences
2010, 3(4): 255–265
