Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review. (June 2022) - Marques, Rita; Andrade De Jesus, Danilo; Barbosa-Breda, João; Van Eijgen, Jan; Stalmans, Ingeborg; van Walsum, Theo; Klein, Stefan; G． Vaz, Pedro; Sánchez Brea, Luisa
Highlights: There is a growing interest in optic nerve head features as biomarkers for disease diagnosis and/or progression. Several parameters can be extracted from automatic segmentations of the optic nerve head OCT scans, which may be relevant for clinical practice. When compared with other approaches, deep learning-based algorithms provide the highest accuracy, sensitivity, and specificity for segmenting the different structures of the optic nerve head including the lamina cribrosa. Efforts should be made to make more OCT data available and to develop standardized guidelines for the extracted parameters and metrics, so that accurate comparisons between methods can be performed. Abstract: The optic nerve head (ONH) represents the intraocular section of the optic nerve, which is prone to damage by intraocular pressure (IOP). The advent of optical coherence tomography (OCT) has enabled the evaluation of novel ONH parameters, namely the depth and curvature of the lamina cribrosa (LC). Together with the Bruch's membrane minimum-rim-width (BMO-MRW), these seem to be promising ONH parameters for diagnosis and monitoring of retinal diseases such as glaucoma. Nonetheless, these OCT derived biomarkers are mostly extracted through manual segmentation, which is time-consuming and prone to bias, thus limiting their usability in clinical practice. The automatic segmentation of ONH in OCT scans could further improve the current clinical management of glaucoma and other diseases. This review summarizes the current state-of-the-art in automatic segmentation of the ONH in OCT. PubMed and Scopus were used to perform a systematic review. Additional works from other databases (IEEE, Google Scholar and ARVO IOVS) were also included, resulting in a total of 29 reviewed studies. For each algorithm, the methods, the size and type of dataset used for validation, and the respective results were carefully analysed. The results show a lack of consensus regarding the definition of segmented regions, extracted parameters and validation approaches, highlighting the importance and need of standardized methodologies for ONH segmentation. Only with a concrete set of guidelines, these automatic segmentation algorithms will build trust in data-driven segmentation models and be able to enter clinical practice.