GlauSeg-Net: Retinal Fundus Medical Image Automatic Segmentation with Multi-task Learning for Glaucoma Early Screening

• Published in: IEEE access p. 1
• Main Authors: Chen, Shuting, Wei, Dezhi, Hong, Chengxi, Li, Li, Qiu, Xiuliang, Jia, Hong
• Format: Journal Article
• Language: English
• Published: IEEE 21.10.2024
• Subjects: Accuracy; Biomedical optical imaging; Cetinal fundus medical image; Feature extraction; Glaucoma; Image segmentation; Integrated optics; multi-task learning; optic cup; optic disc; Optical fiber networks; Optical imaging; Retina; segmentation; Semantics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3484430

Glaucoma is one of the leading causes of irreversible vision loss globally, often resulting in blindness. Early detection and treatment are critical in mitigating its impact, with retinal fundus imaging being the most common method for early screening. Traditionally, glaucoma is diagnosed by examining structural changes in these images, but this process heavily relies on the subjective judgment of clinicians, which can lead to errors. With the advent of artificial intelligence (AI), computer-aided diagnostic systems have emerged as powerful tools for early glaucoma screening, offering diagnostic accuracy comparable to that of expert ophthalmologists. Accurate glaucoma diagnosis from fundus images hinges on the precise calculation of the optic cup-to-disc ratio, which depends on the accurate segmentation of the optic cup and disc. Given that these regions occupy only a small portion of the fundus image, traditional deep learning methods typically approach segmentation in two stages: first, by locating the optic cup and disc through object detection, and then by performing fine-grained segmentation within the identified regions. However, the effectiveness of these methods is often constrained by the initial detection accuracy. In this paper, we introduce a novel one-stage segmentation framework, GlauSeg-Net, specifically designed for early glaucoma screening using retinal fundus images. Our method leverages a multi-task learning strategy that employs weak labels to pre-locate the segmentation target within feature layers, significantly enhancing the performance of small target segmentation. Experimental results demonstrate that proposed GlauSeg-Net get 96.8% and 88.3% segmentation accuracy on optic disc and optic cup respectively, and outperforms mainstream benchmark methods in segmentation accuracy.