Introduction
Glaucoma is a group of neuropathies that irreversibly damage the optic nerve, possibly leading to blindness. Increased intraocular pressure, normally regulated in the anterior chamber angle (ACA) by the trabecular meshwork (TM), is widely recognised as a primary risk factor.1 The underlying cause of a limited TM functionality can be used to categorise the disease and choose the best medical treatment, making the ACA assessment fundamental.1
Current international guidelines recommend gonioscopy to assess the ACA.2 3 However, conventional (manual) gonioscopy presents known limitations, for example, challenging acquisition of digital images and steep learning curve. To overcome these, new devices have been developed.4 5 The acquisition of digital images of the ACA enables automated data analysis, for example, using deep learning (DL).
DL systems have pushed the state-of-the-art in image processing, with numerous applications in ophthalmology. Such systems can be divided into two main categories:
Classification algorithms: assigning each input to one of a set of classes, for example, healthy/pathological sample discrimination in fundus images.6
Segmentation algorithms: performing a pixel-wise classification of inputs, thus highlighting target structures like the vasculature in fundus images or retinal layers in optical coherence tomography (OCT) data.7–9
DL has been used for glaucoma-related tasks on OCT.10 In particular, the detection of angle closure using anterior-segment (AS) OCT cross-sections of the ACA has proved effective.11 12 However, AS-OCT does not allow direct inspection of the ACA surface, preventing the evaluation of important biomarkers, such as neo-vascularisations and TM pigmentation; the detection of anterior synechiae (SY) may also be problematic.
The literature on DL applications to gonio-photographs is much more limited. This imbalance and the complementarity of AS-OCT and gonio-photographs for ACA evaluation support the importance of research in automated gonio-photographs analysis.
The automatic detection of angle closure in gonio-photographs has been studied.13 However, algorithms so far only provide a global characterisation of input images, possibly missing important local features and preventing a comprehensive analysis of layers’ interfaces and their changes over time, for example, for follow-up.
We present a DL system for semantic segmentation of digital gonio-photographs to allow the assessment of local ACA morphology. Our algorithm provides an accurate segmentation of five anatomical layers: iris root (I), ciliary body band (CBB), scleral spur (SS), TM and cornea (C). We discuss the advantages of this model with respect to our previous work,14 which is, to our best knowledge, the only full publication on automated ACA layers segmentation. In particular we address the main limitation in ‘DL-based segmentation of gonioscopic images’,14 regarding results interpretability, in two ways: (1) implementing a region of interest (ROI) localisation module to refine segmentation results and (2) estimating output epistemic uncertainty through a Monte Carlo dropout approach.15 Moreover, we refine the model architecture and fine-tune training hyperparameters to improve the overall segmentation performance.
Our work fills a gap in current research for clinical applications of DL (automated analysis of gonio-photographs). Our algorithm can be used as backbone processing for the measurement of structures of the ACA, for example, size of anterior SY, and their changes over time. Other possible use comprise, but are not limited to, the localisation of the trabecular meshwork, as a preprocessing step for pigmentation grading (eg, prior to laser trabeculoplasty), and the improvement of auto-alignment and auto-tracking systems for data acquisition in non-contact clinical examinations, that are gaining importance due to the COVID-19 pandemic.
It is also worth noting that segmentation algorithms widely employed in other clinical applications are not suitable for off-the-shelf use with our digital gonio-photographs. This is due to ground-truth limitations discussed in the Materials and methods section. Our model design has been specifically conceived to process ground-truth data to maximise output accuracy and interpretability.
This paper is structured as follows: Materials and methods section describes the dataset and the model architecture; Results section compares the performance of the proposed model with those of our previous work14; Discussion section summarises our findings, highlighting improvements and current limitations.