Conventional fundus images (CFIs) and ultra-widefield fundus images (UFIs) are two fundamental image modalities in ophthalmology. While CFIs provide a detailed view of the optic nerve head and the posterior pole of an eye, their clinical use is associated with high costs and patient inconvenience due to the requirement of good pupil dilation. On the other hand, UFIs capture peripheral lesions, but are sensitive to factors such as pupil size, eye position, and eyelashes, leading to greater variability between examinations compared to CFIs. The widefield retina view of UFIs offers the theoretical possibility of generating CFIs from available UFIs to reduce patient examination costs. A recent study has shown the feasibility of this approach by leveraging deep learning techniques for the UFI-to-CFI translation task. However, the technique suffers from the heterogeneous scales of the image modalities and variations in the brightness of the training data. In this paper, we address these issues with a novel framework consisting of three stages: cropping, enhancement, and translation. The first stage is an optic disc-centered cropping strategy that helps to alleviate the scale difference between the two image domains. The second stage mitigates the variation in training data brightness and unifies the mask between the two modalities. In the last stage, we introduce an attention-aided generative learning model to translate a given UFI into the CFI domain. Our experimental results demonstrate the success of the proposed method on 1,011 UFIs, with 99.8% of the generated CFIs evaluated as good quality and usable. Expert evaluations confirm significant visual quality improvements in the generated CFIs compared to the UFIs, ranging from 10% to 80% for features such as optic nerve structure, vascular distribution, and drusen. Furthermore, using generated CFIs in an AI-based diagnosis system for age-related macular degeneration yields superior and competitive accuracy compared to using UFIs and real CFIs, respectively. These results showcase the potential of our approach for automatic disease diagnosis and monitoring.

Ultra-widefield fundus image (UFI) has become a crucial tool for ophthalmologists in diagnosing ocular diseases because of its ability to capture a wide field of the retina. Nevertheless, detecting and classifying multiple diseases within this imaging modality continues to pose a significant challenge for ophthalmologists. An automated disease classification system for UFI can support ophthalmologists in making faster and more precise diagnoses. However, existing works for UFI classification often focus on a single disease or assume each image only contains one disease when tackling multi-disease issues. Furthermore, the distinctive characteristics of each disease are typically not utilized to improve the performance of the classification systems. To address these limitations, we propose a novel approach that leverages disease-specific regions of interest for the multi-label classification of UFI. Our method uses three regions, including the optic disc area, the macula area, and the entire UFI, which serve as the most informative regions for diagnosing one or multiple ocular diseases. Experimental results on a dataset comprising 5930 UFIs with six common ocular diseases showcase that our proposed approach attains exceptional performance, with the area under the receiver operating characteristic curve scores for each class spanning from 95.07% to 99.14%. These results not only surpass existing state-of-the-art methods but also exhibit significant enhancements, with improvements of up to 5.29%. These results demonstrate the potential of our method to provide ophthalmologists with valuable information for early and accurate diagnosis of ocular diseases, ultimately leading to improved patient outcomes.

Increasingly inexpensive unmanned aerial vehicles (UAVs) are helpful for searching and tracking moving objects in ground events. Previous works either have assumed that data about the targets are sufficiently available, or they solely rely on on-board electronics (e.g., camera and radar) to chase them. In a searching mission, path planning is essentially preprogrammed before taking off. Meanwhile, a large-scale wireless sensor network (WSN) is a promising means for monitoring events continuously over immense areas. Due to disadvantageous networking conditions, it is nevertheless hard to maintain a centralized database with sufficient data to instantly estimate target positions. In this paper, we therefore propose an online self-navigation strategy for a UAV-WSN integrated system to supervise moving objects. A UAV on duty exploits data collected on the move from ground sensors together with its own sensing information. The UAV autonomously executes edge processing on the available data to find the best direction toward a target. The designed system eliminates the need of any centralized database (fed continuously by ground sensors) in making navigation decisions. We employ a local bivariate regression to formulate acquired sensor data, which lets the UAV optimally adjust its flying direction, synchronously to reported data and object motion. In addition, we also construct a comprehensive searching and tracking framework in which the UAV flexibly sets its operation mode. As a result, least communication and computation overhead is actually induced. Numerical results obtained from NS-3 and Matlab cosimulations have shown that the designed framework is clearly promising in terms of accuracy and overhead costs.