Quantitative analysis of functional filtering bleb size using Mask R-CNN.

BACKGROUND

Deep learning has had a large effect on medical fields, including ophthalmology. The goal of this study was to quantitatively analyze the functional filtering bleb size with Mask R-CNN.

METHODS

This observational study employed eighty-three images of post-trabeculectomy functional filtering blebs. The images were divided into training and test groups and scored according to the Indiana Bleb Appearance Grading Scale (IBAGS) system. Then, 70 images from the training group were used to train an automatic detection system based on Mask R-CNN and perform a quantitative analysis of the function bleb size. Thirteen images from the test group were used to evaluate the model. During the training process, left and right image-flipping algorithms were used for data augmentation. Finally, the correlation between the functional filtering bleb area and the intraocular pressure (IOP) was analyzed.

RESULTS

The 83 functional filtering blebs have similar morphological features. According to IBAGS, the functional filtering blebs have a high incidence of E1/E2, H1/H2, and V0/V1. Our Mask R-CNN-based model using the selected parameters achieves good results on the training group after a 200-epoch training process. All the Intersection over Union (IoU) scores exceeded 93% on the test group. The Spearman correlation coefficient between the area of functional filtering blebs and the IOP value was -0.757 (P<0.05).

CONCLUSIONS

Deep learning is a powerful tool for quantitatively analyzing the functional filtering bleb size. This technique is suitable for use in monitoring post-trabeculectomy filtering blebs in the future.