A Fully Automated 3D In-Vivo Delineation and Shape Parameterization of the Human Lamina Cribrosa in Optical Coherence Tomography

Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review

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.

Automatic Quantification of Anterior Lamina Cribrosa Structures in Optical Coherence Tomography Using a Two-Stage CNN Framework

In this study, we propose a new intelligent system to automatically quantify the morphological parameters of the lamina cribrosa (LC) of the optical coherence tomography (OCT), including depth, curve depth, and curve index from OCT images. The proposed system consisted of a two-stage deep learning (DL) model, which was composed of the detection and the segmentation models as well as a quantification process with a post-processing scheme. The models were used to solve the class imbalance problem and obtain Bruch's membrane opening (BMO) as well as anterior LC information. The detection model was implemented by using YOLOv3 to acquire the BMO and LC position information. The Attention U-Net segmentation model is used to compute accurate locations of the BMO and LC curve information. In addition, post-processing is applied using polynomial regression to attain the anterior LC curve boundary information. Finally, the numerical values of morphological parameters are quantified from BMO and LC curve information using an image processing algorithm. The average precision values in the detection performances of BMO and LC information were 99.92% and 99.18%, respectively, which is very accurate. A highly correlated performance of R2 = 0.96 between the predicted and ground-truth values was obtained, which was very close to 1 and satisfied the quantification results. The proposed system was performed accurately by fully automatic quantification of BMO and LC morphological parameters using a DL model.

Lamina Cribrosa Depth and Shape in Glaucoma Suspects. Comparison to Glaucoma Patients and Healthy Controls

Purpose: To evaluate the lamina cribrosa depth and shape parameters in glaucoma suspects compared to glaucoma patients and healthy controls. Materials and Methods: A total of 325 subjects (120 with primary open-angle glaucoma, 103 glaucoma suspects and 102 healthy controls) were included. Serial horizontal B-scan images of optic nerve head were obtained using enhanced depth imaging of the optical coherence tomography. For each of the 325 subjects, lamina cribrosa position was measured manually in 16 horizontal B-scans, hence 5200 scans in total were analyzed. In particular, lamina cribrosa depth (LCD), lamina cribrosa deflection depth (LCDD), lamina cribrosa shape index (LCSI), and its horizontal equivalent (LCSI_H) were estimated. Along lamina cribrosa parameterization, intraocular pressure, visual field, central retinal thickness, retinal nerve fiber layer thickness, and disc and neuroretinal rim areas were also measured. Results: LCD was statistically significant different (P < .001) in glaucoma patients when compared to glaucoma suspects and heathy controls (603 ± 172 μm versus 554 ± 114 μm and 531 ± 115 μm, respectively). Similarly, LCDD was statistically significant different (P < .001) in glaucoma patients when compared to glaucoma suspects and heathy controls (250 ± 78 μm versus 213 ± 54 μm and 211 ± 58 μm, respectively). No statistically significant differences were found in LCSI (P = .957). However, LCSI_H showed statistically significant differences between healthy controls and glaucoma suspects (P = .003) and between healthy controls and glaucoma patients (P = .006). Conclusions: The deformation of LC in glaucoma suspects, in terms of LCSI_H, was not statistically different from that of glaucoma patients. LCD does not have the potential to discriminate glaucoma suspects from healthy controls.