Automatic segmentation and quantification of OCT images pre and post cataract surgery using deep learning.

Obtaining quantitative geometry of the anterior segment of the eye, generally from Optical Coherence Tomography (OCT) images, is important to construct 3-D computer eye models, used to understand the optical quality of the normal and pathological eye, and to improve treatment (for example, selecting the intraocular lens to be implanted in cataract surgery, or guiding refractive surgery). An important step to quantify OCT images is segmentation (i.e., finding and labeling the surfaces of interest in the images) which for the purpose of feeding optical models, needs to be automatic, accurate, robust, and fast. In this work, we designed a segmentation algorithm based on deep learning, which we applied to OCT images from pre- and post-cataract surgery eyes obtained using anterior segment OCT commercial systems. We proposed a feature pyramid network architecture with a pre-trained encoder, and trained, validated, and tested the algorithm using 1640 OCT images. We showed that the proposed method outperformed a classical image processing-based approach in terms of accuracy (from 91.4% to 93.2% accuracy), robustness (decreasing the standard deviation of accuracy across images by a factor of 1.7), and processing time (from 0.48 s/image to 0.34 s/image). We also described a method for the 3-D models’ construction and their quantification from the segmented images and applied the proposed segmentation/quantification algorithms to quantify 136 new eye measurements (780 images) obtained from OCT commercial systems.