Retinal vessel segmentation plays an imaportant role in the field of retinal image analysis because changes in retinal vascular structure can aid in the diagnosis of diseases such as hypertension and diabetes. In recent research, numerous successful segmentation methods for fundus images have been proposed. But for other retinal imaging modalities, more research is needed to explore vascular extraction. In this work, we propose an efficient method to segment blood vessels in Scanning Laser Ophthalmoscopy (SLO) retinal images. Inspired by U-Net, "feature map reuse" and residual learning, we propose a deep dense residual network structure called DRNet. In DRNet, feature maps of previous blocks are adaptively aggregated into subsequent layers as input, which not only facilitates spatial reconstruction, but also learns more efficiently due to more stable gradients. Furthermore, we introduce DropBlock to alleviate the overfitting problem of the network. We train and test this model on the recent SLO public dataset. The results show that our method achieves the state-of-the-art performance even without data augmentation .
With the rapid development of the Internet of Things, the requirements for massive data processing technology are getting higher and higher. Traditional computer data processing capabilities can no longer deliver fast, simple, and efficient data analysis and processing for today’s massive data processing due to the real-time, massive, polymorphic, and heterogeneous characteristics of Internet of Things data. Mass heterogeneous data of different types of subsystems in the Internet of Things need to be processed and stored uniformly, so the mass data processing method is required to be able to integrate multiple different networks, multiple data sources, and heterogeneous mass data and be able to perform processing on these data. Therefore, this article proposes massive data processing and multidimensional database management based on deep learning to meet the needs of contemporary society for massive data processing. This article has deeply studied the basic technical methods of massive data processing, including MapReduce technology, parallel data technology, database technology based on distributed memory databases, and distributed real-time database technology based on cloud computing technology, and constructed a massive data fusion algorithm based on deep learning. The model and the multidimensional online analytical processing model of the multidimensional database based on deep learning analyze the performance, scalability, load balancing, data query, and other aspects of the multidimensional database based on deep learning. It is concluded that the accuracy of multidimensional database query data is as high as 100%, and the accuracy of the average data query time is only 0.0053 s, which is much lower than the general database query time.
The small size of robotic microswimmers makes them suitable for performing biomedical tasks in tiny, enclosed spaces. Considering the effects of potentially long-term retention of microswimmers in biological tissues and the environment, the degradability of microswimmers has become one of the pressing issues in this field. While degradable hydrogel was successfully used to prepare microswimmers in previous reports, most hydrogel microswimmers could only be fabricated using two-photon polymerization (TPP) due to their 3D structures, resulting in costly robotic microswimmers solution. This limits the potential of hydrogel microswimmers to be used in applications where a large number of microswimmers are needed. Here, we proposed a new type of preparation method for degradable hydrogel achiral crescent microswimmers using a custom-built stop-flow lithography (SFL) setup. The degradability of the hydrogel crescent microswimmers was quantitatively analyzed, and the degradation rate in sodium hydroxide solution (NaOH) of different concentrations was investigated. Cytotoxicity assays showed the hydrogel crescent microswimmers had good biocompatibility. The hydrogel crescent microswimmers were magnetically actuated using a 3D Helmholtz coil system and were able to obtain a swimming efficiency on par with previously reported microswimmers. The results herein demonstrated the potential for the degradable hydrogel achiral microswimmers to become a candidate for microscale applications.
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