A Deep Learning Approach to Detect the Demarcation Line in OCT Images

Chadi Helwe, Shady Elbassuoni, Ahmad R. Dhaini, Lily Chacra, Shady Awwad

Abstract: Corneal cross-linking (CXL) is a surgical intervention to treat the progression of an eye disease called keratoconus that may lead to significant loss of visual acuity. Manually detecting the presence and the depth of a stromal demarcation line in optical coherence tomography (OCT) images is a standard procedure used by ophthalmologists to check the success of CXL. In this paper, we propose a deep learning model trained in a semi-weakly supervised fashion to segment the area between the top boundary of the cornea and the demarcation line that is later used by our extraction algorithm to obtain the demarcation line automatically. We report an improvement in performance compared to the fully supervised learning approaches in terms of the dice coefficient.