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 o...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.展开更多
Dear Editor,The rising prevalence of myopia has become a significant global public health issue,with pathologic myopia emerging as a major cause of irreversible vision impairment,particularly in China(Baird et al.,202...Dear Editor,The rising prevalence of myopia has become a significant global public health issue,with pathologic myopia emerging as a major cause of irreversible vision impairment,particularly in China(Baird et al.,2020).Fundus tessellation(FT),the earliest stage of myopic macular degeneration(MMD),serves as a key predictor for disease progression,as its severity correlates with choroidal thinning,axial elongation,and increased myopic refractive error(Foo et al.,2023b).展开更多
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.
基金supported by the National Natural Science Foundation of China(82220108017,82141128,82401283)the Capital Health Research and Development of Special(2024-1-2052)+3 种基金Science&Technology Project of Beijing Municipal Science&Technology Commission(Z201100005520045)Sanming Project of Medicine in Shenzhen(SZSM202311018)Scientific Research Common Program of Beijing Municipal Commission of Education(KM202410025011)the priming scientific research foundation for the junior researcher in Beijing Tongren Hospital,Capital Medical University(2023-YJJ-ZZL-003)。
文摘Dear Editor,The rising prevalence of myopia has become a significant global public health issue,with pathologic myopia emerging as a major cause of irreversible vision impairment,particularly in China(Baird et al.,2020).Fundus tessellation(FT),the earliest stage of myopic macular degeneration(MMD),serves as a key predictor for disease progression,as its severity correlates with choroidal thinning,axial elongation,and increased myopic refractive error(Foo et al.,2023b).
