Multi-Discriminator Adversarial Convolutional Network for Nerve Fiber Segmentation in Confocal Corneal Microscopy Images

Quantitative measurements of corneal sub-basal nerves are biomarkers for many ocular surface disorders and are also important for early diagnosis and assessment of progression of neurodegenerative diseases. This paper aims to develop an automatic method for nerve fiber segmentation from in vivo corneal confocal microscopy (CCM) images, which is fundamental for nerve morphology quantification. A novel multi-discriminator adversarial convolutional network (MDACN) is proposed, where both the generator and the two discriminators emphasize multi-scale feature representations. The generator is a U-shaped fully convolutional network with multi-scale split and concatenate blocks, and the two discriminators have different effective receptive fields, sensitive to features of different scales. A novel loss function is also proposed which enables the network to pay more attention to thin fibers. The MDACN framework was evaluated on four datasets. Experiment results show that our method has excellent segmentation performance for corneal nerve fibers and outperforms some state-of-the-art methods.

Automated summary

This paper aims to develop an automatic method for nerve fiber segmentation from in vivo corneal confocal microscopy (CCM) images. A novel multi-discriminator adversarial convolutional network (MDACN) framework is proposed, and experimental results show that the method has excellent segmentation performance for corneal nerve fibers.