Han Li

Diabetic retinopathy (DR) is a common chronic fundus disease, which has four different kinds of microvessel structure and microvascular lesions: microaneurysms (MAs), hemorrhages (HEs), hard exudates, and soft exudates. Accurate detection and counting of them are a basic but important work. The manual annotation of these lesions is a labor-intensive task in clinical analysis. To solve the problem, we proposed a novel segmentation method for different lesions in DR. Our method is based on a convolutional neural network and can be divided into encoder module, attention module, and decoder module, so we refer it as EAD-Net. After normalization and augmentation, the fundus images were sent to the EAD-Net for automated feature extraction and pixel-wise label prediction. Given the evaluation metrics based on the matching degree between detected candidates and ground truth lesions, our method achieved sensitivity of 92.77%, specificity of 99.98%, and accuracy of 99.97% on the e_ophtha_EX dataset and comparable AUPR (Area under Precision-Recall curve) scores on IDRiD dataset. Moreover, the results on the local dataset also show that our EAD-Net has better performance than original U-net in most metrics, especially in the sensitivity and F1-score, with nearly ten percent improvement. The proposed EAD-Net is a novel method based on clinical DR diagnosis. It has satisfactory results on the segmentation of four different kinds of lesions. These effective segmentations have important clinical significance in the monitoring and diagnosis of DR.

High myopia is a global ocular disease and one of the most common causes of blindness. Fundus images can be obtained in a noninvasive manner and can be used to monitor and follow up on many fundus diseases, such as high myopia. In this paper, we proposed a computer-aided diagnosis algorithm using deep convolutional neural networks (DCNNs) to grade the risk of high myopia. The input images were automatically classified into three categories: normal fundus images were labeled class 0, low-risk high-myopia images were labeled class 1, and high-risk high-myopia images were labeled class 2. We conducted model training on 758 clinical fundus images collected locally, and the average accuracy reached 98.15% according to the results of fivefold cross-validation. An additional 100 fundus images were used to evaluate the performance of DCNNs, with ophthalmologists performing external validation. The experimental results showed that DCNNs outperformed human experts with an area under the curve (AUC) of 0.9968 for the recognition of low-risk high myopia and 0.9964 for the recognition of high-risk high myopia. In this study, we were able to accurately and automatically perform high myopia classification solely using fundus images. This has great practical significance in terms of improving early diagnosis, prevention, and treatment in clinical practice.

Detailed extraction of retinal vessel morphology is of great significance in many clinical applications. In this paper, we propose a retinal image segmentation method, called MAU-Net, which is based on the U-net structure and takes advantages of both modulated deformable convolution and dual attention modules to realize vessels segmentation. Specifically, based on the classic U-shaped architecture, our network introduces the Modulated Deformable Convolutional (MDC) block as encoding and decoding unit to model vessels with various shapes and deformations. In addition, in order to obtain better feature presentations, we aggregate the outputs of dual attention modules: the position attention module (PAM) and channel attention module (CAM). On three publicly available datasets: DRIVE, STARE and CHASEDB1, we have achieved superior performance to other algorithms. Quantitative and qualitative experimental results show that our MAU-Net can effectively and accurately accomplish the retinal vessels segmentation task.

Abstract Ozone (O 3 ) generated by a dielectric barrier discharge (DBD) is widely used in various industrial processes. In this study, NaCl aqueous solution was used as a novel electric power transmission electrode in a DBD reactor (instead of a traditional metal electrode) for highly efficient ozone generation. The results demonstrated that a high O 3 yield of 242 g kWh −1 with a concentration of 14.6 g m −3 O 3 was achieved. The power transmission mechanism works because NaCl aqueous solution behaves as a capacitor when an alternating pulse voltage below 8 kHz is used. Compared with the resistance of the discharge barrier and discharge space, the resistance of NaCl aqueous solution can be ignored, which ensures that O 3 is generated efficiently. It is expected that O 3 generation using NaCl aqueous solution as a novel electrode in a DBD reactor could be an alternative technology with good application prospects.