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
Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys-UC), Department of Physics, University of Coimbra, Coimbra, Portugal; Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.
Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.
Comput Methods Programs Biomed. 2022 Jun;220:106801. doi: 10.1016/j.cmpb.2022.106801. Epub 2022 Apr 6.
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.
视神经乳头（ONH）代表视神经的眼内部分，易受眼内压（IOP）损害。光学相干断层扫描（OCT）的出现使得对视神经乳头新参数的评估成为可能，即筛板（LC）的深度和曲率。连同布鲁赫膜最小边缘宽度（BMO - MRW），这些似乎是用于诊断和监测青光眼等视网膜疾病的有前景的视神经乳头参数。然而，这些源自OCT的生物标志物大多通过手动分割提取，既耗时又容易产生偏差，从而限制了它们在临床实践中的可用性。OCT扫描中视神经乳头的自动分割可以进一步改善青光眼和其他疾病的当前临床管理。本综述总结了OCT中视神经乳头自动分割的当前技术水平。使用PubMed和Scopus进行系统综述。还纳入了来自其他数据库（IEEE、谷歌学术和ARVO IOVS）的其他研究，总共审查了29项研究。对于每种算法，仔细分析了用于验证的方法、数据集的大小和类型以及各自的结果。结果表明，在分割区域的定义、提取的参数和验证方法方面缺乏共识，突出了视神经乳头分割标准化方法的重要性和必要性。只有有一套具体的指导方针，这些自动分割算法才能建立对数据驱动分割模型的信任并能够进入临床实践。
