ScLNet: A cornea with scleral lens OCT layers segmentation dataset and new multi-task model

Objective: To develop deep learning methods with high accuracy for segmenting irregular corneas and detecting the tear fluid reservoir (TFR) boundary under the scleral lens. Additionally, this study aims to provide a publicly available cornea with scleral lens OCT dataset, including manually labeled layer masks for training and validation of segmentation algorithms. This study introduces ScLNet, a dataset comprising cornea with Scleral Lens (ScL) optical coherence tomography (OCT) images with layer annotations, and a multi-task network designed to achieve rapid, accurate, automated segmentation of scleral lens with regular and irregular corneas. Methods: We created a dataset comprising 31,360 OCT images with scleral lens annotations. The network architecture includes an encoder with multi-scale input and a context coding layer, along with two decoders for specific tasks. The primary task focuses on predicting ScL, TFR, and cornea regions, while the auxiliary task, aimed at predicting the boundaries of ScL, TFR, and cornea, enhances feature extraction for the main task. Segmentation results were compared with state-of-the-art methods and evaluated using Dice similarity coefficient (DSC), intersection over union (IoU), Matthews correlation coefficient (MCC), Precision, and Hausdorff distance (HD). Results: ScLNet achieves 98.22 % DSC, 96.50 % IoU, 98.13 % MCC, 98.35 % Precision, and 3.6840 HD (in pixels) in segmenting ScL; 97.78 % DSC, 95.66 % IoU, 97.71 % MCC, 97.70 % Precision, and 3.7838 HD (in pixels) in segmenting TFR; and 99.22 % DSC, 98.45 % IoU, 99.15 % MCC, 99.14 % Precision, and 3.5355 HD (in pixels) in segmenting cornea. The layer interfaces recognized by ScLNet closely align with expert annotations, as evidenced by high IoU scores. Boundary metrics further confirm its effectiveness. Conclusion: We constructed a dataset of corneal OCT images with ScL wearing, which includes regular and irregular cornea patients. The proposed ScLNet achieves high accuracy in extracting ScL, TFR, and corneal layer masks and boundaries from OCT images of the dataset.