Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide,including diseases associated with corneal pathologies,anterior chamber abnormalities(e.g.blood or inflammat...Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide,including diseases associated with corneal pathologies,anterior chamber abnormalities(e.g.blood or inflammation),and lens diseases.The construction of an automatic tool for segmentation of anterior segment eye lesions would greatly improve the efficiency of clinical care.With research on artificial intelligence progressing in recent years,deep learning models have shown their superiority in image classification and segmentation.The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise;however,such data are relatively scarce in the domain of medicine.Herein,the authors developed a new medical image annotation system,called EyeHealer.It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level.Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation.The results showed that semantic segmentation models outperformed medical segmentation models.This paper describes the establishment of the system for automated classification and segmentation tasks.The dataset will be made publicly available to encourage future research in this area.展开更多
AIM: To quantify the changes in the lens profile with accommodation in different age groups. METHODS: The Pentacam HR system was used to obtain the images of the anterior eye segment from 23 young and 15 presbyopic em...AIM: To quantify the changes in the lens profile with accommodation in different age groups. METHODS: The Pentacam HR system was used to obtain the images of the anterior eye segment from 23 young and 15 presbyopic emmetropic subjects in unaccommodated (with an accommodation stimulus of 0.0D) and accommodated (with an accommodation stimulus of 5.0D for the young group and 1.0D for the presbyopic group) states. The phakic crystalline lens shape, including curvature of crystalline lens and central lens thickness (CLT), and the measurements of anterior segment length (ASL), central anterior chamber depth (CACD) were investigated. The anterior chamber volume (ACV) was also measured. RESULTS: The reduction of CACD and ACV were significant in both groups after accommodation stimulus. From the profile of anterior eye segment, a significant decrease in anterior crystalline lens radii of curvature (-2.52mm) and a mean increase in CLT (0.222mm) and ASL (0.1138mm) were found in the. young group with an accommodation stimulus of 5.0D. However, no statistically significant changes of CLT, ASL, or crystalline lens radii of curvature were found in the presbyopic group. CONCLUSION: Our data showed that the shallowing of anterior chamber during accommodation was caused by the forward bulging of the anterior lens surface, rather than by anterior shifting of lens position in either young or presbyopic subjects.展开更多
基金This study was funded by the National Key Research and Development Program of China(Grant No.2017YFC1104600)Recruitment Program of Leading Talents of Guangdong Province(Grant No.2016LJ06Y375).
文摘Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide,including diseases associated with corneal pathologies,anterior chamber abnormalities(e.g.blood or inflammation),and lens diseases.The construction of an automatic tool for segmentation of anterior segment eye lesions would greatly improve the efficiency of clinical care.With research on artificial intelligence progressing in recent years,deep learning models have shown their superiority in image classification and segmentation.The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise;however,such data are relatively scarce in the domain of medicine.Herein,the authors developed a new medical image annotation system,called EyeHealer.It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level.Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation.The results showed that semantic segmentation models outperformed medical segmentation models.This paper describes the establishment of the system for automated classification and segmentation tasks.The dataset will be made publicly available to encourage future research in this area.
基金Supported by National Natural Science Foundation of China (No. 81070747)Research Award for New Century Excellent Talents in University (No. NCET08-0586)in ChinaScience and Technology Planning Project of Guangdong Province, China (No.2010B090400416)
文摘AIM: To quantify the changes in the lens profile with accommodation in different age groups. METHODS: The Pentacam HR system was used to obtain the images of the anterior eye segment from 23 young and 15 presbyopic emmetropic subjects in unaccommodated (with an accommodation stimulus of 0.0D) and accommodated (with an accommodation stimulus of 5.0D for the young group and 1.0D for the presbyopic group) states. The phakic crystalline lens shape, including curvature of crystalline lens and central lens thickness (CLT), and the measurements of anterior segment length (ASL), central anterior chamber depth (CACD) were investigated. The anterior chamber volume (ACV) was also measured. RESULTS: The reduction of CACD and ACV were significant in both groups after accommodation stimulus. From the profile of anterior eye segment, a significant decrease in anterior crystalline lens radii of curvature (-2.52mm) and a mean increase in CLT (0.222mm) and ASL (0.1138mm) were found in the. young group with an accommodation stimulus of 5.0D. However, no statistically significant changes of CLT, ASL, or crystalline lens radii of curvature were found in the presbyopic group. CONCLUSION: Our data showed that the shallowing of anterior chamber during accommodation was caused by the forward bulging of the anterior lens surface, rather than by anterior shifting of lens position in either young or presbyopic subjects.