Purely Image-based Vault Prediction with Domain Prior Supervision for Intraocular Lens Implantation

Myopia is the most common eye disorder in the world, and posterior chamber phakic intraocular lens implantation, as a myopia correction surgery, has been widely used in clinics due to its reversibility, wide range of correction degree, and retention of the lens adjustment ability. We address the problem of vault prediction, which assists to ensure the safety of this myopia correction surgery. The existing methods need to measure the eye parameters first and then use the regression method, which is very time-consuming and has subjective errors. Thus, we aim to design an automatic deep learning-based method for vault prediction only considering anterior segment optical coherence tomography (AS-OCT) images. Specifically, a deep neural network is utilized to extract the image features, and then a regression module is designed to predict the vault. Furthermore, we introduce domain prior supervision into the deep learning framework. Anterior chamber structure segmentation obtained by semi-supervised learning is considered to provide additional structural features. The prediction of auxiliary measurements, which are related to the vault, is designed to deeply supervise the learning process. Experiments on our dataset (465 test samples) show that the proposed method can reduce the mean absolute error by 39.36-57.34 and 7.39-9.20 compared with the multiple regression methods and machine learning-based methods, respectively. These results show that it is promising to predict vault using AS-OCT images without parameter measurement.