A Deep Learning-Based Study on Automatic Measurement Method of Biological Parameters in Anterior Segment UBM Images of Angle-Closure Glaucoma.
Authors
Affiliations (1)
Affiliations (1)
- State Key Laboratory of Advanced Medical Materials and Devices, Institute of Biomedical Engineering, Tianjin Institutes of Health Science, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin, China.
Abstract
This study aimed to develop a deep learning-based model for measuring biometric parameters from anterior segment ultrasound biomicroscopy (UBM) images, assisting clinicians in the early screening and diagnosis of primary angle-closure glaucoma (PACG). Through comparative selection, the best-performing model was used to segment four regions in UBM images of PACG patients: cornea and sclera, iris, ciliary body, and lens anterior surface. Using YOLOv11s, the model performed localization of four key points on both pre- and post-segmentation datasets and automatically measured seven biometric parameters related to the angle closure mechanism. The DeepLabv3+ model performed excellently in the segmentation tasks, with an mean intersection over union (mIoU) of 85.84%, precision of 92.67%, recall of 91.36%, and Dice coefficient of 92.01%, all representing optimal values. In the object detection task, the segmented dataset achieved a precision of 88.3%, recall of 89.9%, and mean average precision at 0.50 IoU (mAP50) of 92.9%, showing at least a 19% improvement over the original image dataset. The average absolute error of the Euclidean distance for four-point localization was 50.41µm, with a root mean square error of 77.85 µm. In biometric parameter measurements, except for the iris-lens angle, all other parameters exhibited intraclass correlation coefficient (ICC) values greater than 0.94. This study showed that the proposed deep learning-based method for automatic measurement of closure mechanism-related biometric parameters is accurate and effective. This is the first study, to our knowledge, to integrate multi-region segmentation and four-point localization for the automatic computation of seven anterior segment parameters directly from UBM images, providing precise and reliable data support for early PACG diagnosis and pathogenesis research.