Optical coherence tomography (OCT) is an imaging modality that acquires high‐resolution cross‐sectional images of living tissues and it has become the standard in ophthalmological diagnoses. However, most quantitative morphological measurements are based on the raw OCT images which are distorted by several mechanisms such as the refraction of probe light in the sample and the scan geometries and thus the analysis of the raw OCT images inevitably induced calculation errors. In this paper, based on Fermat's principle and the concept of inverse light tracing, image distortions due to refraction occurred at tissue boundaries in the whole‐eye OCT imaging of mouse by telecentric scanning were corrected. Specially, the mathematical correction models were deducted for each interface, and the high‐precision whole‐eye image was recovered segment by segment. We conducted phantom and in vivo experiments on mouse and human eyes to verify the distortion correction algorithm, and several parameters of the radius of curvature, thickness of tissues and error, were calculated to quantitatively evaluate the images. Experimental results demonstrated that the method can provide accurate and reliable measurements of whole‐eye parameters and thus be a valuable tool for the research and clinical diagnosis.
Optical coherence tomography angiography (OCTA) has been widely used in clinical fields because of its noninvasive, high-resolution qualities. Accurate vessel segmentation on OCTA images plays an important role in disease diagnosis. Most deep learning methods are based on region segmentation, which may lead to inaccurate segmentation for the extremely complex curve structure of retinal vessels. We propose a U-shaped network called SS-Net that is based on the attention mechanism to solve the problem of continuous segmentation of discontinuous vessels of a retinal OCTA. In this SS-Net, the improved SRes Block combines the residual structure and split attention to prevent the disappearance of gradient and gives greater weight to capillary features to form a backbone with an encoder and decoder architecture. In addition, spatial attention is applied to extract key information from spatial dimensions. To enhance the credibility, we use several indicators to evaluate the function of the SS-Net. In two datasets, the important indicators of accuracy reach 0.9258/0.9377, respectively, and a Dice coefficient is achieved, with an improvement of around 3% compared to state-of-the-art models in segmentation.
An increasing number of experiments had verified that miRNA expression is related to human diseases. The miRNA expression profile may be an indicator of clinical diagnosis and provides a new direction for the prevention and treatment of complex diseases. In this work, we present a weighted voting-based model for predicting miRNA–disease association (WVMDA). To reasonably build a network of similarity, we established credibility similarity based on the reliability of known associations and used it to improve the original incomplete similarity. To eliminate noise interference as much as possible while maintaining more reliable similarity information, we developed a filter. More importantly, to ensure the fairness and efficiency of weighted voting, we focus on the design of weighting. Finally, cross-validation experiments and case studies are undertaken to verify the efficacy of the proposed model. The results showed that WVMDA could efficiently identify miRNAs associated with the disease.
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