Dataset and Evaluation algorithm design for GOALS Challenge

TL;DR

This paper introduces the GOALS Challenge, providing a dataset, evaluation methods, and baselines for AI research in OCT-based glaucoma diagnosis and layer segmentation, aiming to advance the field through community participation.

Contribution

It presents a new dataset, evaluation framework, and baseline results for OCT image analysis in glaucoma, fostering AI research in this medical domain.

Findings

300 OCT images released for research

Baseline models established for segmentation and classification

Evaluation methodology defined for challenge comparison

Abstract

Glaucoma causes irreversible vision loss due to damage to the optic nerve, and there is no cure for glaucoma.OCT imaging modality is an essential technique for assessing glaucomatous damage since it aids in quantifying fundus structures. To promote the research of AI technology in the field of OCT-assisted diagnosis of glaucoma, we held a Glaucoma OCT Analysis and Layer Segmentation (GOALS) Challenge in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 to provide data and corresponding annotations for researchers studying layer segmentation from OCT images and the classification of glaucoma. This paper describes the released 300 circumpapillary OCT images, the baselines of the two sub-tasks, and the evaluation methodology. The GOALS Challenge is accessible at https://aistudio.baidu.com/aistudio/competition/detail/230.