Automatic simultaneous ciliary muscle segmentation and biomarker extraction in AS-OCT images using deep learning-based approaches

Abstract

Recent clinical studies have emphasized the importance of understanding the morphology and mechanics of the ciliary muscle. The ciliary muscle plays a vital role in various functions related to the anterior segment of the eye, including the regulation of intraocular pressure and the maintenance of the shape of the crystalline lens. To advance research in this area, we propose a fully automated methodology for the segmentation and biomarker measurement of the ciliary muscle in two different scan depths (6mm and 16mm), which are commonly used by clinicians to analyze biomarkers. Our methodology aims to provide repeatable, and immediate results through an exhaustive analysis of different network architectures, encoders, and transfer learning strategies. We also extracted a comprehensive set of relevant biomarkers, including parameters that provide essential information about its behavior during the accommodation process, overall dimensions, and biomechanical properties. These biomarkers can help clinicians and researchers in the diagnoses and monitor of different ocular diseases such as glaucoma, myopia, and presbyopia and develop new therapeutic strategies, potentially leading to more effective treatments and improved patient outcomes. Our methodology achieved accurate qualitative and quantitative results, with high accuracy values of 0.9665±0.1280 and 0.9772±0.0873 for the best combinations for 6mm and 16mm, respectively. Our proposed system provides a valuable and automatic tool for clinicians and researchers in the segmentation and analysis of the ciliary muscle in AS-OCT images.