| 1 | Long-term temporal-spatial dynamics of marine coastal water quality in the Tolo Harbor,Hong Kong,China显示文摘The long-term temporal and spatial dynamics of marine coastal water quality in Tolo Harbor, Hong Kong were explored. The Harbor is divided into three zones represented as Harbor, Buffer, and Channel Subzones. The time range for the study covers the period from the 1970s to the 1990s. The selected indicators for the comprehensive assessment of water quality consist of physical, chemical and biological aspects, including suspended solids(SS), Secchi disk depth(SD), 5-day biochemical oxygen demand(BOD\-5), total nitrogen(TN), total phosphorus(TP), faecal coliform, chlorophyll-a(Chl-a), and the number of red tide occurrences. The results indicated the presence of obvious temporal and spatial trends with regard to changes in water quality. Spatially, water quality in the Channel Subzone is the best, while that in the Harbor Subzone is the worst. On a temporal basis, the average trend from bad to good was 1980s>1990s>1970s as indicated by most of the selected water quality indicators. Water quality during the late 1980s reached its worst level with the lowest SD, the highest BOD\-5, TN, TP, Chl-a concentrations, and the number of red tide occurrences. These long-term temporal-spatial water quality trends were also found in other studies of the Tolo Harbor. The large quantity of pollutants produced as a result of increasing population, industrial and commercial actives, and urbanization and industrialization trends in both Shatin and Tai Po seem to be primarily responsible for the changes in marine coastal water quality. | XUFu-liu K.C.LAM R.W.Dawson TAOShu Y.D.CHEN | 2004 | Journal of Environmental Sciences2004，16，1： | 4 |
| 2 | Automatic quantification of superficial foveal avascular zone in optical coherence tomography angiography implemented with deep learning显示文摘An accurate segmentation and quantification of the superficial foveal avascular zone(sFAZ)is important to facilitate the diagnosis and treatment of many retinal diseases,such as diabetic retinopathy and retinal vein occlusion.We proposed a method based on deep learning for the automatic segmentation and quantification of the sFAZ in optical coherence tomography angiography(OCTA)images with robustness to brightness and contrast(B/C)variations.A dataset of 405 OCTA images from 45 participants was acquired with Zeiss Cirrus HD-OCT 5000 and the ground truth(GT)was manually segmented subsequently.A deep learning network with an encoder–decoder architecture was created to classify each pixel into an sFAZ or non-sFAZ class.Subsequently,we applied largestconnected-region extraction and hole-filling to fine-tune the automatic segmentation results.A maximum mean dice similarity coefficient(DSC)of 0.976±0.011 was obtained when the automatic segmentation results were compared against the GT.The correlation coefficient between the area calculated from the automatic segmentation results and that calculated from the GT was 0.997.In all nine parameter groups with various brightness/contrast,all the DSCs of the proposed method were higher than 0.96.The proposed method achieved better performance in the sFAZ segmentation and quantification compared to two previously reported methods.In conclusion,we proposed and successfully verified an automatic sFAZ segmentation and quantification method based on deep learning with robustness to B/C variations.For clinical applications,this is an important progress in creating an automated segmentation and quantification applicable to clinical analysis. | Menglin Guo Mei Zhao Allen M.Y.Cheong Houjiao Dai Andrew K.C.Lam Yongjin Zhou | 2019 | Visual Computing for Industry,Biomedicine,and Art2019，2，1： | 2 |
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