Automated Image Quality Assessment for Anterior Segment Optical Coherence Tomograph

Optical Coherence Tomography (OCT) is a technique for diagnosing eye disorders. Image quality assessment (IQA) of OCT images is essential, but manual IQA is time consuming and subjective. Recently, automated IQA methods based on deep learning (DL) have achieved good performance. However, few of these methods focus on OCT images of the anterior segment of the eye (AS-OCT). Moreover, few of these methods identify the factors that affect the quality of the images (called "quality factors" in this paper). This could adversely affect the acceptance of their results. In this study, we define, for the first time to the best of our knowledge, the quality level and four quality factors of AS-OCT for the clinical context of anterior chamber inflammation. We also develop an automated framework based on multi-task learning to assess the quality and to identify the existing of quality factors in the AS-OCT images. The effectiveness of the framework is demonstrated in experiments