Scribble-Supervised Meibomian Glands Segmentation in Infrared Images

Infrared imaging is currently the most effective clinical method to evaluate the morphology of the meibomian glands (MGs) in patients. As an important indicator for monitoring the development of MG dysfunction, it is necessary to accurately measure gland-drop and gland morphology. Although there are existing methods for automatic segmentation of MGs using deep learning frameworks, they require fully annotated ground-truth labels for training, which is time-consuming and laborious. In this article, we proposed a new scribble-supervised deep learning framework for segmenting the MGs, which only requires easily attainable scribble annotations for training. To cope with the shortage of supervision and regularize the network, a transformation consistent strategy is incorporated, which requires the prediction to follow the same transformation if a transform is performed on an input image of the network. The proposed segmentation method consists of two stages. In the first stage, a U-Net network is used to obtain the meibomian region segmentation map. In the second stage, we concentrate on segmenting glands in the meibomian region. We utilize the gradient prior information of the original image at the decoder part of the segmentation network, which can coarsely locate the target contour. We automatically generate reliable labels using the exponential moving average of the predictions during training and filter out the unreliable pseudo-label by uncertainty threshold. Experimental results on a local MG dataset and two other public medical image datasets demonstrate the effectiveness of the proposed segmentation framework.