Enhancing meibography based assessment of gland morphology by utilizing an image-rotating Mask R-CNN approach

• The segmentation approach for the characterisation of meibomian glands was proposed. • The approach proves to be effective for the two leading meibography devices. • Image postprocessing improves the quality of the segmentation. • Gland segmentation metrics: IoU of 70.5 % and AJI of 66.3 % were achieved. Accurate analysis of meibomian gland morphology based on meibography images is of great importance for the diagnosis of dry eye disease. However, it is still a difficult task due to the time-consuming and variability of traditional methods. Nowadays, machine learning-based approaches are becoming increasingly common. Nevertheless, there is currently no universal model for evaluating images from different devices, and the segmentation quality is still insufficient. In this study, a novel approach is used that combines the DeepLabV3 neural network for tarsus segmentation with an image-rotating implementation of the Mask R-CNN neural network for segmenting meibomian gland instances. The proposed method achieves an average Jaccard index of 66.3% and an intersection over union of 70.5% on the test dataset, outperforming other methods while demonstrating competitive performance for the two leading meibography devices. Furthermore, the proposed approach enables precise determination of glandular morphological parameters, including length, thickness, tortuosity, and glands area ratio. These findings highlight the diagnostic value of Mask R-CNN in providing effective and standardised assessments of meibomian gland morphology.