Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings
Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings
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眼科医学图像分析 - 第七届国际研讨会，OMIA 2020，与 MICCAI 2020 联合举行，秘鲁利马，2020 年 10 月 8 日，会议记录
DOI：
10.1007/978-3-030-63419-3_16
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Giarratano Y
中科院分区：
文献类型：
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作者：
Giarratano Y
最近的研究已经证明了OCTA视网膜成像在发现眼睛和其他器官血管疾病的生物标志物方面的潜力。此外，深度学习的进步使得训练自动检测这些生物标志物的算法成为可能。然而，这种方法的两个关键限制是需要大量标记的图像来训练算法，这通常是文献中典型的单中心前瞻性研究无法满足的，并且缺乏训练过程中学习到的特征的可解释性。在当前的研究中，我们开发了一个网络分析框架来表征视网膜血管系统，其中利用几何和拓扑信息来提高在数十张OCTA图像上训练的分类器的性能。我们在两种不同的视网膜血管足迹疾病中展示了我们的方法：糖尿病视网膜病变（DR）和慢性肾脏疾病（CKD）。我们的方法能够发现以前未报道的DR和CKD视网膜血管形态差异，并证明OCTA在自动疾病评估中的潜力。
Recent studies have demonstrated the potential of OCTA retinal imaging for the discovery of biomarkers of vascular disease of the eye and other organs. Furthermore, advances in deep learning have made it possible to train algorithms for the automated detection of such biomarkers. However, two key limitations of this approach are the need for large numbers of labeled images to train the algorithms, which are often not met by the typical single-centre prospective studies in the literature, and the lack of interpretability of the features learned during training. In the current study, we developed a network analysis framework to characterise retinal vasculature where geometric and topological information are exploited to increase the performance of classifiers trained on tens of OCTA images. We demonstrate our approach in two different diseases with a retinal vascular footprint: diabetic retinopathy (DR) and chronic kidney disease (CKD). Our approach enables the discovery of previously unreported retinal vascular morphological differences in DR and CKD, and demonstrate the potential of OCTA for automated disease assessment.