论文标题

伽玛挑战:多模式图像的青光眼分级

GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges

论文作者

Wu, Junde, Fang, Huihui, Li, Fei, Fu, Huazhu, Lin, Fengbin, Li, Jiongcheng, Huang, Lexing, Yu, Qinji, Song, Sifan, Xu, Xinxing, Xu, Yanyu, Wang, Wensai, Wang, Lingxiao, Lu, Shuai, Li, Huiqi, Huang, Shihua, Lu, Zhichao, Ou, Chubin, Wei, Xifei, Liu, Bingyuan, Kobbi, Riadh, Tang, Xiaoying, Lin, Li, Zhou, Qiang, Hu, Qiang, Bogunovic, Hrvoje, Orlando, José Ignacio, Zhang, Xiulan, Xu, Yanwu

论文摘要

色眼摄影和光学相干断层扫描(OCT)是青光眼筛查的两种最具成本效益的工具。两种图像的两种模式都有明显的生物标志物,以表明怀疑青光眼。在临床上,通常建议您进行两个筛查,以进行更准确和可靠的诊断。但是,尽管基于底底图像或计算机辅助诊断中的OCT量提出了许多算法,但仍很少有方法利用两种方法来进行青光眼评估。受到我们先前举行的视网膜眼底青光眼挑战(避难所)的成功的启发,我们从多模式图像(Gamma)挑战中建立了青光眼分级,以鼓励基于基于眼底的基于基于基础的Glaucoma等级的发展。挑战的主要任务是从2D底面图像和3D OCT扫描量的卷素级的青光眼进行评分。作为Gamma的一部分,我们已公开发布了带有2D Felcus Color Photography和3d Oct卷的青光眼注释数据集,这是第一个用于青光眼分级的多模式数据集。此外,还建立了评估框架以评估提交方法的性能。在挑战期间,提交了1272个结果,最后,将前10支球队选为最后阶段。我们分析他们的结果,并在论文中总结他们的方法。由于所有这些团队在挑战中提交了源代码,因此还进行了详细的消融研究,以验证提出的特定模块的有效性。我们发现许多提出的技术对于青光眼的临床诊断都是实用的。作为对眼底\&OCT多模式青光眼分级的首次深入研究,我们认为伽马挑战挑战将是未来研究的重要起点。

Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarkers to indicate glaucoma suspected. Clinically, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment. Inspired by the success of Retinal Fundus Glaucoma Challenge (REFUGE) we held previously, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus \& OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, top-10 teams were selected to the final stage. We analysis their results and summarize their methods in the paper. Since all these teams submitted their source code in the challenge, a detailed ablation study is also conducted to verify the effectiveness of the particular modules proposed. We find many of the proposed techniques are practical for the clinical diagnosis of glaucoma. As the first in-depth study of fundus \& OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will be an essential starting point for future research.

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