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RISS 인기검색어
- 검색키워드
- 저자 : Chen Zetao (검색결과 2 건)
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A Standardized Rat Model to Study Peri-implantitis of Transmucosal Osseointegrated Implants
Xingchen Liu,Shudan Deng,Xiyan Li,Haiwen Liu,Zhixin Li,You Wu,Pu Luo,Xinyi Zhong,Ruoxuan Huang,Runheng Liu,Xiayi Wu,Baoxin Huang,Zetao Chen,Zhuofan Chen,Shoucheng Chen 한국생체재료학회 2024 생체재료학회지 Vol.28 No.00
With the high incidence rate, distinctive implant characteristic and unique infection pattern, peri-implantitis (PI) requires a specially designed implant animal model for the researches on the pathogenesis and treatments. Previous small-animal PI models exhibit variability in implant site selection, design, and surgical procedures resulting in unnecessary tissue damage and less effectivity. Herein, a quantitative-analysis-based standardized rat model for transmucosal PI-related research was proposed. After dissecting the anatomic structures of the rat maxilla, we determined that placing the implant anterior to the molars in the rat maxilla streamlined the experimental period and enhanced animal welfare. We standardized the model by controlling the rat strain, gender, and size. The customized implant and a series of matched surgical instruments were appropriately designed. A clear, step-by-step surgical process was established. These designs ensured the success rate, stability, and replicability of the model. Each validation method confirmed the successful construction of the model. This study proposed a quantitative-analysis-based standardized transmucosal PI rat model with improved animal welfare and reliable procedures. This model could provide efficient in vivo insights to study the pathogenesis and treatments of PI and preliminary screening data for further large-animal and clinical trials.
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Instance segmentation with pyramid integrated context for aerial objects
Juan Wang,Liquan Guo,Minghu Wu,Guanhai Chen,Zishan Liu,Yonggang Ye,Zetao Zhang 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.3
Aerial objects are more challenging to segment than normal objects, which are usually smaller and have less textural detail. In the process of segmentation, target objects are easily omitted and misdetected, which is problematic. To alleviate these issues, we propose local aggregation feature pyramid networks (LAFPNs) and pyramid integrated context modules (PICMs) for aerial object segmentation. First, using an LAFPN, while strengthening the deep features, the extent to which low-level features interfere with high-level features is reduced, and numerous dense and small aerial targets are prevented from being mistakenly detected as a whole. Second, the PICM uses global information to guide local features, which enhances the network's comprehensive understanding of an entire image and reduces the missed detection of small aerial objects due to insufficient texture information. We evaluate our network with the MS COCO dataset using three categories: airplanes, birds, and kites. Compared with Mask R-CNN, our network achieves performance improvements of 1.7%, 4.9%, and 7.7% in terms of the AP metrics for the three categories. Without pretraining or any postprocessing, the segmentation performance of our network for aerial objects is superior to that of several recent methods based on classic algorithms.
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