Performance assessment of an injectable hyaluronic acid/polylactic acid complex hydrogel with enhanced biological properties as a dermal filler.

Injectable hyaluronic acid (HA) hydrogel plays an important role in dermal filling. However, conventional HA dermal fillers mostly lack bio-functional diversity and frequently cause adverse reactions because of the chemical stiffness of highly modified degree and crosslinker residues. In this study, polylactic acid (PLA) was embedded into HA hydrogel as a bioactive substance and 1,4-butanediol diglycidyl ether was used as a crosslinker to prepare the HA/PLA composite hydrogel with enhanced biocompatibility and biological performance. We aimed to investigate the properties of HA/PLA composite hydrogels as dermal fillers by assessing the rheological properties, surface microstructure, enzymolysis stability, swelling ratio, degradation rate, cytotoxicity, and anti-wrinkle effect on photo-aged skin. The results showed that the stability and stiffness of the composite hydrogel decreased with an increasing amount of PLA, while the in vivo safety of the HA/PLA hydrogel was enhanced, showing no adverse reactions such as edema, redness, or swelling. Moreover, the composite hydrogel with 2 wt% PLA exhibited excellent anti-wrinkle effects, showing the highest collagen production. Thus, the PLA-embedded HA composite hydrogel showed potential as a dermal filler with high safety, easy injectability, and excellent anti-wrinkle effects.

Analysis of segmentation of lung parenchyma based on deep learning methods.

Precise segmentation of lung parenchyma is essential for effective analysis of the lung. Due to the obvious contrast and large regional area compared to other tissues in the chest, lung tissue is less difficult to segment. Special attention to details of lung segmentation is also needed. To improve the quality and speed of segmentation of lung parenchyma based on computed tomography (CT) or computed tomography angiography (CTA) images, the 4th International Symposium on Image Computing and Digital Medicine (ISICDM 2020) provides interesting and valuable research ideas and approaches. For the work of lung parenchyma segmentation, 9 of the 12 participating teams used the U-Net network or its modified forms, and others used the methods to improve the segmentation accuracy include attention mechanism, multi-scale feature information fusion. Among them, U-Net achieves the best results including that the final dice coefficient of CT segmentation is 0.991 and the final dice coefficient of CTA segmentation is 0.984. In addition, attention U-Net and nnU-Net network also performs well. In this paper, the methods chosen by 12 teams from different research groups are evaluated and their segmentation results are analyzed for the study and references to those involved.

Automated vessel segmentation in lung CT and CTA images via deep neural networks.

BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation rate and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.

A Pulmonary Artery-Vein Separation Algorithm Based on the Relationship between Subtrees Information.

Physicians need to distinguish between pulmonary arteries and veins when diagnosing diseases such as chronic obstructive pulmonary disease (COPD) and lung tumors. However, manual differentiation is difficult due to various factors such as equipment and body structure. Unlike previous geometric methods of manually selecting the points of seeds and using neural networks for separation, this paper proposes a combined algorithm for pulmonary artery-vein separation based on subtree relationship by implementing a completely new idea and combining global and local information, anatomical knowledge, and two-dimensional region growing method. The algorithm completes the reconstruction of the whole vascular structure and the separation of adhesion points from the tree-like structure characteristics of blood vessels, after which the automatic classification of arteries and veins is achieved by using anatomical knowledge, and the whole process is free from human intervention. After comparing all the experimental results with the gold standard, we obtained an average separation accuracy of 85%, which achieved effective separation. Meanwhile, the time range could be controlled between 40 s and 50 s, indicating that the algorithm has good stability.

Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning.

Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks.

Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network.

The COVID-19 coronavirus has spread rapidly around the world and has caused global panic. Chest CT images play a major role in confirming positive COVID-19 patients. The computer aided diagnosis of COVID-19 from CT images based on artificial intelligence have been developed and deployed in some hospitals. But environmental influences and the movement of lung will affect the image quality, causing the lung parenchyma and pneumonia area unclear in CT images. Therefore, the performance of COVID-19's artificial intelligence diagnostic algorithm is reduced. If chest CT images are reconstructed, the accuracy and performance of the aided diagnostic algorithm may be improved. In this paper, a new aided diagnostic algorithm for COVID-19 based on super-resolution reconstructed images and convolutional neural network is presented. Firstly, the SRGAN neural network is used to reconstruct super-resolution images from original chest CT images. Then COVID-19 images and Non-COVID-19 images are classified from super-resolution chest CT images by VGG16 neural network. Finally, the performance of this method is verified by the pubic COVID-CT dataset and compared with other aided diagnosis methods of COVID-19. The experimental results show that improving the data quality through SRGAN neural network can greatly improve the final classification accuracy when the data quality is low. This proves that this method can obtain high accuracy, sensitivity and specificity in the examined test image datasets and has similar performance to other state-of-the-art deep learning aided algorithms.