A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection.

Journal: Mathematical biosciences and engineering : MBE
Published Date:

Abstract

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it is determined by dividing the area of the optic cup (OC) by that of the optic disc (OD) in fundus images. Consequently, the automatic and accurate segmentation of the OC and OD is a pivotal step in glaucoma detection. In recent years, numerous methods have resulted in great success on this task. However, most existing methods either have unsatisfactory segmentation accuracy or high time costs. In this paper, we propose a lightweight deep-learning architecture for the simultaneous segmentation of the OC and OD, where we have adopted fuzzy learning and a multi-layer perceptron to simplify the learning complexity and improve segmentation accuracy. Experimental results demonstrate the superiority of our proposed method as compared to most state-of-the-art approaches in terms of both training time and segmentation accuracy.

Authors

  • Yantao Song
    Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006, China.
  • Wenjie Zhang
    Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, People's Republic of China.
  • Yue Zhang
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.