# Dataset and Evaluation algorithm design for GOALS Challenge

arXiv: https://arxiv.org/abs/2207.14447 · HTML: https://mlsanity.com/paper/2207.14447/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 29 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [OCTID: Optical Coherence Tomography Image Database](https://mlsanity.com/paper/1812.07056/) (2018)
- [Glaucoma Detection From Raw Circumapillary OCT Images Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/2006.00027/) (2020)

## Built on by (most cited first, 13 of 13)

- [MedSAM3: Delving into Segment Anything with Medical Concepts](https://mlsanity.com/paper/2511.19046/) (2025)
- [Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning](https://mlsanity.com/paper/2308.13411/) (2023)
- [M2SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation](https://mlsanity.com/paper/2303.10894/) (2023)
- [PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation](https://mlsanity.com/paper/2305.07816/) (2023)
- [MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images](https://mlsanity.com/paper/2412.09402/) (2024)
- [A Pragmatic Note on Evaluating Generative Models with Fr'echet Inception Distance for Retinal Image Synthesis](https://mlsanity.com/paper/2502.17160/) (2025)
- [RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation](https://mlsanity.com/paper/2501.08458/) (2025)
- [MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis](https://mlsanity.com/paper/2506.08900/) (2025)
- [Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation](https://mlsanity.com/paper/2311.05479/) (2023)
- [Can Multimodal Large Language Models Understand OCT?](https://mlsanity.com/paper/2607.16609/) (2026)
- [AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review](https://mlsanity.com/paper/2410.15947/) (2024)
- [LUMOS: Universal Semi-Supervised OCT Retinal Layer Segmentation with Hierarchical Reliable Mutual Learning](https://mlsanity.com/paper/2604.05388/) (2026)
- [C^2GR: Coupled Comprehensive Generative Replay for a Continually Learnable Universal Segmentation Model](https://mlsanity.com/paper/2606.23473/) (2026)

## Abstract (arXiv)

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.