Meibomian Glands Segmentation In Near-Infrared Images With Weakly Supervised Deep Learning

Abstract

Near-infrared imaging is currently the most effective clinical method for evaluating the morphology of the meibomian glands in patients. Meibomian gland dysfunction (MGD) is a chronic and diffuse disease of the meibomian glands, which is an important cause of eye diseases such as dry-eye and blepharitis. Therefore, it is important to monitor the glanddrop and gland morphology for MGD patients. In this paper, we proposed a new scribble-supervised deep learning method for segmenting the meibomian glands. The proposed segmentation network consists of two stages. The first stage uses the U-Net network to obtain the meibomian region segmentation map. The second stage focuses on the meibomian region, combining spatial attention, gradient map and label filtering to generate the meibomian gland segmentation results. Experimental results on a local meibomian gland dataset demonstrate the effectiveness of the proposed segmentation framework.

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“…Manual annotation can be exceedingly time-consuming and labor-intensive, considering the considerable number and close arrangement of glands. Even for experienced ophthalmologists, the process can take an average of 5 to 8min per image [13] .…”