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Meibomian glands dysfunction (MGD) is the main cause of dry eyes. The degree of meibomian gland atrophy plays an important role in the clinical diagnosis of MGD. The automatic quantification of meibomian gland area (MGA) and meibomian gland atrophy area (MGAA) is challenging due to the blurred boundary and various shapes. A U-shaped information fusion network (IF-Net) is proposed for the segmentation of MGA and MGAA in this paper. The contributions of this paper are as follows: (1) An information fusion (IF) module is designed to fuse the context information from the spatial dimension and the channel dimension respectively, which effectively reduces the loss of information caused by continuous downsampling. (2) A parallel path connection (PPC) is proposed and inserted into skip connections. On one hand, it can suppress the noise of different levels of information. On the other hand, it can make up the lack of information via the original simple skip connection of U-Net. Our proposed IF-Net has been evaluated on 505 infrared MG images from 300 subjects and achieves the average Dice similarity coefficient (DSC) of 84.81% and the average intersection over union (IoU) of 74.44% on MGAA segmentation, which indicates the primary effectiveness of the proposed method.
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Dengfeng Liu, Weifang Zhu, Xinyu Zhuang, Xiaofeng Zhang, Xinjian Chen, Fei Shi, Dehui Xiang, "IF-Net: information fusion network for meibomian gland area and atrophy area segmentation," Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124643X (3 April 2023); https://doi.org/10.1117/12.2654206