A Novel Adaptive Weighted Loss Design in Adversarial Learning for Retinal Nerve Fiber Layer Defect Segmentation

Shuai Lu,Ruirui Li,Man Hu,Yongli Xu

doi:10.1109/access.2020.3009442

Abstract

Glaucoma is a chronic eye disease that can cause permanent visual loss and is difficult to detect early. Retinal nerve fiber layer defect (RNFLD) is clinical evidence for the diagnosis of glaucoma. Classical deep learning based methods can be used to segment RNFLD from fundus images. However, the segmentation results of these methods do not have the specific geometry of RNFLD, and the segmentation errors of fundus images with special styles are large. In this paper, we present a novel conditional adversarial shuffle U-shaped network (CASU-Net) to segment RNFLD, which consists of a generator and a discriminator. For the generator, a mixed loss is designed, which consists of an adaptive weighted segmentation loss and an adversarial loss. This adaptive weighted segmentation loss can balance the segmentation accuracy of the target and background region, and assign more attention to the hard samples, thus ensuring the consistent improvement of the segmentation accuracy of all fundus images. The adversarial loss not only helps to improve the pixel-wise segmentation accuracy but also makes the geometry of the RNFLD segmentation closer to the ground truth. In addition, in the generator, a shuffle module was designed to fully mine the information of all channels to improve the feature extraction capability of the model. The proposed CASU-Net is verified on a RNFLD dataset from Beijing Tongren Hospital. The experiments show that the CASU-Net achieves state-of-the-art results on this dataset.

Highlights

Glaucoma is the second causing blindness disease in the world, and it will cause irreversible visual loss to patients
To solve the above three problems in segmenting Retinal nerve fiber layer defect (RNFLD) in fundus images, we proposed a novel method based on adversarial learning
We propose a novel CASU-Net framework for the segmentation of RNFLD in fundus images

Summary

INTRODUCTION

Glaucoma is the second causing blindness disease in the world, and it will cause irreversible visual loss to patients. A number of works have been proposed to segment the RNFLD These methods mainly include image segmentation methods based on traditional techniques and segmentation. S. Lu et al.: Novel Adaptive Weighted Loss Design in Adversarial Learning for RNFLD Segmentation. To solve the above three problems in segmenting RNFLD in fundus images, we proposed a novel method based on adversarial learning. The main contributions of this work include: 1) We designed a novel conditional adversarial shuffle U-shaped network (CASU-Net), which consists of a generator and a discriminator. 2) In the CASU-Net, an adaptive weighted (AW) segmentation loss was designed for the generator. 4) We evaluate the effectiveness and generalization capability of the proposed CASU-Net and existing methods on a RNFLD dataset from Beijing Tongren Hospital.

RELATED WORK

METHODS

DATASETS AND EVALUATION METHOD

CONCLUSION