Automated segmentation of pterygium lesions using multiscale deep learning networks

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Best performing model achieved 16.75 pixels Hausdorff distance

• Primary Outcome: Segmentation accuracy measured by Hausdorff distance

• Nnt Or Sensitivity: Sensitivity and specificity not reported - technical validation study

• Confidence Interval: Not reported

Clinical Application

Requires specialised imaging equipment and computational resources for implementation Could support ophthalmologists and optometrists in rural/remote areas where specialist expertise is limited. Relevant to Medicare item numbers for pterygium assessment and surgical planning Patients with pterygium requiring severity assessment and monitoring

Abstract

Pterygium is an eye condition that needs to be identified at an early stage so that its progression can be mitigated in order to avoid the possible threat of visual impairment. One of the important low-level modules in determining the severity level of the pterygium automatically is to measure the extent of fibrovascular tissue encroachment onto the corneal and pupil regions. Therefore, it is important to develop a semantic segmentation method to map and quantify the lesion tissues accurately, especially through the deep learning technique. As the pterygium condition progresses from the trace stage to the severe stage, the lesions will generally become bigger but maintain more or less the same shape, which looks like a wedge. Taking inspiration from this unique pattern of pterygium lesions, this work aims to explore multiscale deep learning networks to capture the variable scales of the lesion features effectively. All the proposed multiscale modules are embedded at the bottleneck layer of the base UNet architecture, whereby the feature map size is the smallest to reduce the potential increment in the computational burden. Apart from that, the total number of convolutional channels is fixed to 1024, whereby the multiscale module replaces the first convolutional set at the UNet's bottleneck layer. Two main multiscale modules have been explored, which are spatial pyramid pooling (SPP) and atrous spatial pyramid pooling (ASPP) modules. Furthermore, each of these modules has been expanded by analyzing the equal-flow (EF) and waterfall-flow (WF) patterns in constructing the parallel paths that represent the multiscale features. According to the simulation results, the best segmentation network is UNet, which has been embedded with three parallel paths of the EF-ASPP module that produces the lowest Hausdorff distance of 16.75 pixels. As a result, the severity level of pterygium can be better predicted through accurate extraction of the lesion map. This work can be further improved by analyzing different network architectures of the multiscale module, especially networks that focuses on the larger feature maps size, but surely with a trade-off in computational burden.

References

1. 1.Zulkifley, M. A. (2026). Automated segmentation of pterygium lesions using multiscale deep learning networks. Experimental Eye Research, Article 110928. https://doi.org/10.1016/j.exer.2026.110928