Objective To investigate the clinical features of multifocal choroiditis (MC) and guide the diagnosis and treatment. Methods Retrospective analysis of clinical data of 18 MC cases (28 eyes) who were diagnosed through fluorescein angiography (FFA) or indocyanine green angiography (ICGA) and fundus characteristics. Results Multiple round to oval lesions scattered throughout the posterior pole and peripheral areas of ocular fundi of all of the 28 eyes(binocular in 10 and monocular in 8) were found. Active focal lesions of ocular fundi were seen in 8 patients and inactive lesions in 10 patients. active and 10 cases were inactive. Choroidal neovascularization(CNV) in macular area was found in 7 patients. The images of FFA of the legions showed hypofluorescence in the early phase, with late leakage and gradual staining or window is defect in the late phase. Conclusions MC is a rare disease and often misdiagnosed to other disease and FFA helpful in diagnosis. (Chin J Ocul Fundus Dis, 2005, 21: 367-370)
Microaneurysm is the initial symptom of diabetic retinopathy. Eliminating this lesion can effectively prevent diabetic retinopathy in the early stage. However, due to the complex retinal structure and the different brightness and contrast of fundus image because of different factors such as patients, environment and acquisition equipment, the existing detection algorithms are difficult to achieve the accurate detection and location of the lesion. Therefore, an improved detection algorithm of you only look once (YOLO) v4 with Squeeze-and-Excitation networks (SENet) embedded was proposed. Firstly, an improved and fast fuzzy c-means clustering algorithm was used to optimize the anchor parameters of the target samples to improve the matching degree between the anchors and the feature graphs; Then, the SENet attention module was embedded in the backbone network to enhance the key information of the image and suppress the background information of the image, so as to improve the confidence of microaneurysms; In addition, an spatial pyramid pooling was added to the network neck to enhance the acceptance domain of the output characteristics of the backbone network, so as to help separate important context information; Finally, the model was verified on the Kaggle diabetic retinopathy dataset and compared with other methods. The experimental results showed that compared with other YOLOv4 network models with various structures, the improved YOLOv4 network model could significantly improve the automatic detection results such as F-score which increased by 12.68%; Compared with other network models and methods, the automatic detection accuracy of the improved YOLOv4 network model with SENet embedded was obviously better, and accurate positioning could be realized. Therefore, the proposed YOLOv4 algorithm with SENet embedded has better performance, and can accurately and effectively detect and locate microaneurysms in fundus images.