This article describes a novel configuration design for a re-imaging off-axis catadioptric space infrared optical system,and in order to satisfy the signal noise ratio requirements of the system,the stray light of the...This article describes a novel configuration design for a re-imaging off-axis catadioptric space infrared optical system,and in order to satisfy the signal noise ratio requirements of the system,the stray light of the system is necessary to analyze and restrain. The optical system with a focal length of 1 200 mm,an entrance pupil diameter of 600 mm,an F-number of 2,a field of view of 3°× 0. 15°,a working wave band of 8 μm-10 μm,and the image quality of the optical system almost approach to diffraction limits in all field of view.Then the mathematical models of stray light are built,and the suppressive structure is established to eliminate the effect of stray light. Finally,TraceP ro is used to analyze and simulate stray light with and without the suppressive structure,and also get the results of the PST curves. The results indicate that appropriate optical system and suppressive structure can highly reduce the stray light of the space infrared optical system.展开更多
Purpose: To report the rubeosis iridis and neovascular glaucoma findings in one patient of X-linked juvenile retinoschisis (XLRS).Methods: Color fundus photography, fluorescein angiography (FFA), OCT and B-scan were p...Purpose: To report the rubeosis iridis and neovascular glaucoma findings in one patient of X-linked juvenile retinoschisis (XLRS).Methods: Color fundus photography, fluorescein angiography (FFA), OCT and B-scan were performed in a patient with X-linked juvenile retinoschisis complicated with neovascular glaucoma.Result: Color fundus photography, fluorescein angiography (FFA), OCT and B-scan unveiled a rare condition of XLRS complicated with neovascular glaucoma.Conclusion: XLRS may complicate with neovascular glaucoma. It is necessary to test OCT, FFA, ERG and carefully examine the fundus of the follow eye when it comes to uncertain neovascular glaucoma of youth and child. And only in this way, can we exclude XLRS.展开更多
The Chinese Hα Solar Explorer(CHASE), dubbed “Xihe”—Goddess of the Sun, was launched on October 14, 2021 as the first solar space mission of China National Space Administration(CNSA). The CHASE mission is designed...The Chinese Hα Solar Explorer(CHASE), dubbed “Xihe”—Goddess of the Sun, was launched on October 14, 2021 as the first solar space mission of China National Space Administration(CNSA). The CHASE mission is designed to test a newly developed satellite platform and to acquire the spectroscopic observations in the Hα waveband. The Hα Imaging Spectrograph(HIS)is the scientific payload of the CHASE satellite. It consists of two observational modes: raster scanning mode and continuum imaging mode. The raster scanning mode obtains full-Sun or region-of-interest spectral images from 6559.7 to 6565.9 ? and from 6567.8 to 6570.6 ? with 0.024 ? pixel spectral resolution and 1 min temporal resolution. The continuum imaging mode obtains photospheric images in continuum around 6689 ? with the full width at half maximum of 13.4 ?. The CHASE mission will advance our understanding of the dynamics of solar activity in the photosphere and chromosphere. In this paper, we present an overview of the CHASE mission including the scientific objectives, HIS instrument overview, data calibration flow, and first results of on-orbit observations.展开更多
Background:Retinopathy of prematurity(ROP)is a leading cause of childhood blindness worldwide but can be a treatable retinal disease with appropriate and timely diagnosis.This study was performed to develop a robust i...Background:Retinopathy of prematurity(ROP)is a leading cause of childhood blindness worldwide but can be a treatable retinal disease with appropriate and timely diagnosis.This study was performed to develop a robust intelligent system based on deep learning to automatically classify the severity of ROP from fundus images and detect the stage of ROP and presence of plus disease to enable automated diagnosis and further treatment.Methods:A total of 36,231 fundus images were labeled by 13 licensed retinal experts.A 101-layer convolutional neural network(ResNet)and a faster region-based convolutional neural network(Faster-RCNN)were trained for image classification and identification.We applied a 10-fold cross-validation method to train and optimize our algorithms.The accuracy,sensitivity,and specificity were assessed in a four-degree classification task to evaluate the performance of the intelligent system.The performance of the system was compared with results obtained by two retinal experts.Moreover,the system was designed to detect the stage of ROP and presence of plus disease as well as to highlight lesion regions based on an object detection network using Faster-RCNN.Results:The system achieved an accuracy of 0.903 for the ROP severity classification.Specifically,the accuracies in discriminating normal,mild,semi-urgent,and urgent were 0.883,0.900,0.957,and 0.870,respectively;the corresponding accuracies of the two experts were 0.902 and 0.898.Furthermore,our model achieved an accuracy of 0.957 for detecting the stage of ROP and 0.896 for detecting plus disease;the accuracies in discriminating stage I to stage V were 0.876,0.942,0.968,0.998 and 0.999,respectively.Conclusions:Our system was able to detect ROP and differentiate four-level classification fundus images with high accuracy and specificity.The performance of the system was comparable to or better than that of human experts,demonstrating that this system could be used to support clinical decisions.展开更多
The Hα imaging spectrograph(HIS) is the scientific payload of the first solar space mission, the Chinese Hα solar explorer(CHASE), supported by the China National Space Administration(CNSA). The CHASE/HIS achieves, ...The Hα imaging spectrograph(HIS) is the scientific payload of the first solar space mission, the Chinese Hα solar explorer(CHASE), supported by the China National Space Administration(CNSA). The CHASE/HIS achieves, for the first time in space, Hα spectroscopic observations with high spectral and temporal resolutions. Separate channels for the raster scanning mode(RSM) and continuum imaging mode(CIM) are integrated into one, and a highly integrated design is achieved through multiple folding of the optical path and ultra-light miniaturized components. The design of HIS implements a number of key technologies such as high-precision scanning of the optical field of view(FOV), high-precision integrated manufacturing inspection, a large-tolerance pre-filter window, and full-link solar radiation calibration. The HIS instrument has a pixel spectral resolution of 0.024 ? and can complete a full-Sun scanning within 46 s.展开更多
Background:Retinopathy of prematurity(ROP)is a leading cause of childhood blindness worldwide but can be a treatable retinal disease with appropriate and timely diagnosis.This study was performed to develop a robust i...Background:Retinopathy of prematurity(ROP)is a leading cause of childhood blindness worldwide but can be a treatable retinal disease with appropriate and timely diagnosis.This study was performed to develop a robust intelligent system based on deep learning to automatically classify the severity of ROP from fundus images and detect the stage of ROP and presence of plus disease to enable automated diagnosis and further treatment.Methods:A total of 36,231 fundus images were labeled by 13 licensed retinal experts.A 101-layer convolutional neural network(ResNet)and a faster region-based convolutional neural network(Faster-RCNN)were trained for image classification and identification.We applied a 10-fold cross-validation method to train and optimize our algorithms.The accuracy,sensitivity,and specificity were assessed in a four-degree classification task to evaluate the performance of the intelligent system.The performance of the system was compared with results obtained by two retinal experts.Moreover,the system was designed to detect the stage of ROP and presence of plus disease as well as to highlight lesion regions based on an object detection network using Faster-RCNN.Results:The system achieved an accuracy of 0.903 for the ROP severity classification.Specifically,the accuracies in discriminating normal,mild,semi-urgent,and urgent were 0.883,0.900,0.957,and 0.870,respectively;the corresponding accuracies of the two experts were 0.902 and 0.898.Furthermore,our model achieved an accuracy of 0.957 for detecting the stage of ROP and 0.896 for detecting plus disease;the accuracies in discriminating stage I to stage V were 0.876,0.942,0.968,0.998 and 0.999,respectively.Conclusions:Our system was able to detect ROP and differentiate four-level classification fundus images with high accuracy and specificity.The performance of the system was comparable to or better than that of human experts,demonstrating that this system could be used to support clinical decisions.展开更多
基金Sponsored by the National High Technology Research and Development Program of China(Grant No.863-2-5-1-13B)
文摘This article describes a novel configuration design for a re-imaging off-axis catadioptric space infrared optical system,and in order to satisfy the signal noise ratio requirements of the system,the stray light of the system is necessary to analyze and restrain. The optical system with a focal length of 1 200 mm,an entrance pupil diameter of 600 mm,an F-number of 2,a field of view of 3°× 0. 15°,a working wave band of 8 μm-10 μm,and the image quality of the optical system almost approach to diffraction limits in all field of view.Then the mathematical models of stray light are built,and the suppressive structure is established to eliminate the effect of stray light. Finally,TraceP ro is used to analyze and simulate stray light with and without the suppressive structure,and also get the results of the PST curves. The results indicate that appropriate optical system and suppressive structure can highly reduce the stray light of the space infrared optical system.
文摘Purpose: To report the rubeosis iridis and neovascular glaucoma findings in one patient of X-linked juvenile retinoschisis (XLRS).Methods: Color fundus photography, fluorescein angiography (FFA), OCT and B-scan were performed in a patient with X-linked juvenile retinoschisis complicated with neovascular glaucoma.Result: Color fundus photography, fluorescein angiography (FFA), OCT and B-scan unveiled a rare condition of XLRS complicated with neovascular glaucoma.Conclusion: XLRS may complicate with neovascular glaucoma. It is necessary to test OCT, FFA, ERG and carefully examine the fundus of the follow eye when it comes to uncertain neovascular glaucoma of youth and child. And only in this way, can we exclude XLRS.
基金supported by China National Space Administration(CNSA)。
文摘The Chinese Hα Solar Explorer(CHASE), dubbed “Xihe”—Goddess of the Sun, was launched on October 14, 2021 as the first solar space mission of China National Space Administration(CNSA). The CHASE mission is designed to test a newly developed satellite platform and to acquire the spectroscopic observations in the Hα waveband. The Hα Imaging Spectrograph(HIS)is the scientific payload of the CHASE satellite. It consists of two observational modes: raster scanning mode and continuum imaging mode. The raster scanning mode obtains full-Sun or region-of-interest spectral images from 6559.7 to 6565.9 ? and from 6567.8 to 6570.6 ? with 0.024 ? pixel spectral resolution and 1 min temporal resolution. The continuum imaging mode obtains photospheric images in continuum around 6689 ? with the full width at half maximum of 13.4 ?. The CHASE mission will advance our understanding of the dynamics of solar activity in the photosphere and chromosphere. In this paper, we present an overview of the CHASE mission including the scientific objectives, HIS instrument overview, data calibration flow, and first results of on-orbit observations.
基金This work was supported by:The National Key R&D Program of China(Grant No.2017YFE0103400)The National Nature Science Foundation of China(Grant No.81470628).
文摘Background:Retinopathy of prematurity(ROP)is a leading cause of childhood blindness worldwide but can be a treatable retinal disease with appropriate and timely diagnosis.This study was performed to develop a robust intelligent system based on deep learning to automatically classify the severity of ROP from fundus images and detect the stage of ROP and presence of plus disease to enable automated diagnosis and further treatment.Methods:A total of 36,231 fundus images were labeled by 13 licensed retinal experts.A 101-layer convolutional neural network(ResNet)and a faster region-based convolutional neural network(Faster-RCNN)were trained for image classification and identification.We applied a 10-fold cross-validation method to train and optimize our algorithms.The accuracy,sensitivity,and specificity were assessed in a four-degree classification task to evaluate the performance of the intelligent system.The performance of the system was compared with results obtained by two retinal experts.Moreover,the system was designed to detect the stage of ROP and presence of plus disease as well as to highlight lesion regions based on an object detection network using Faster-RCNN.Results:The system achieved an accuracy of 0.903 for the ROP severity classification.Specifically,the accuracies in discriminating normal,mild,semi-urgent,and urgent were 0.883,0.900,0.957,and 0.870,respectively;the corresponding accuracies of the two experts were 0.902 and 0.898.Furthermore,our model achieved an accuracy of 0.957 for detecting the stage of ROP and 0.896 for detecting plus disease;the accuracies in discriminating stage I to stage V were 0.876,0.942,0.968,0.998 and 0.999,respectively.Conclusions:Our system was able to detect ROP and differentiate four-level classification fundus images with high accuracy and specificity.The performance of the system was comparable to or better than that of human experts,demonstrating that this system could be used to support clinical decisions.
基金supported by the China National Space Administration(CNSA)。
文摘The Hα imaging spectrograph(HIS) is the scientific payload of the first solar space mission, the Chinese Hα solar explorer(CHASE), supported by the China National Space Administration(CNSA). The CHASE/HIS achieves, for the first time in space, Hα spectroscopic observations with high spectral and temporal resolutions. Separate channels for the raster scanning mode(RSM) and continuum imaging mode(CIM) are integrated into one, and a highly integrated design is achieved through multiple folding of the optical path and ultra-light miniaturized components. The design of HIS implements a number of key technologies such as high-precision scanning of the optical field of view(FOV), high-precision integrated manufacturing inspection, a large-tolerance pre-filter window, and full-link solar radiation calibration. The HIS instrument has a pixel spectral resolution of 0.024 ? and can complete a full-Sun scanning within 46 s.
基金supported by the National Key R&D Program of China(Grant No.2017YFE0103400)the National Nature Science Foundation of China(Grant No.81470628).
文摘Background:Retinopathy of prematurity(ROP)is a leading cause of childhood blindness worldwide but can be a treatable retinal disease with appropriate and timely diagnosis.This study was performed to develop a robust intelligent system based on deep learning to automatically classify the severity of ROP from fundus images and detect the stage of ROP and presence of plus disease to enable automated diagnosis and further treatment.Methods:A total of 36,231 fundus images were labeled by 13 licensed retinal experts.A 101-layer convolutional neural network(ResNet)and a faster region-based convolutional neural network(Faster-RCNN)were trained for image classification and identification.We applied a 10-fold cross-validation method to train and optimize our algorithms.The accuracy,sensitivity,and specificity were assessed in a four-degree classification task to evaluate the performance of the intelligent system.The performance of the system was compared with results obtained by two retinal experts.Moreover,the system was designed to detect the stage of ROP and presence of plus disease as well as to highlight lesion regions based on an object detection network using Faster-RCNN.Results:The system achieved an accuracy of 0.903 for the ROP severity classification.Specifically,the accuracies in discriminating normal,mild,semi-urgent,and urgent were 0.883,0.900,0.957,and 0.870,respectively;the corresponding accuracies of the two experts were 0.902 and 0.898.Furthermore,our model achieved an accuracy of 0.957 for detecting the stage of ROP and 0.896 for detecting plus disease;the accuracies in discriminating stage I to stage V were 0.876,0.942,0.968,0.998 and 0.999,respectively.Conclusions:Our system was able to detect ROP and differentiate four-level classification fundus images with high accuracy and specificity.The performance of the system was comparable to or better than that of human experts,demonstrating that this system could be used to support clinical decisions.
