arXiv 2407.122712024-07-17cs.CVeess.IV

RBAD：视网膜血管分支角度检测的数据集与基准

RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection

School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Mayo Clinic（梅奥诊所）；School of Computing, Clemson University（克莱姆森大学计算学院）；Department of Electrical and Computer Engineering, Stevens Institute of Technology（史蒂文斯理工学院电气与计算机工程系）

Hao Wang, Wenhui Zhu, Jiayou Qin, Xin Li, Oana Dumitrascu, Xiwen Chen, Peijie Qiu, Abolfazl Razi

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AI总结：

本文提出一种基于自配置图像处理技术的视网膜分支角度检测方法，并构建了包含40张标注图像的基准数据集RBAD，验证了该方法在多种条件下的高准确性和效率，为眼科研究与临床提供有力工具。

摘要：

视网膜图像分析，特别是分支点的几何特征检测，在眼科疾病诊断中起着至关重要的作用。然而，现有用于此目的的方法通常较为粗糙，缺乏用于高效标注的细粒度分析。为解决这些问题，本文提出了一种使用自配置图像处理技术检测视网膜分支角度的新方法。此外，我们提供了一个开源标注工具和一个包含40张标注了视网膜分支角度图像的基准数据集。我们详细介绍了视网膜分支角度检测与计算的方法，随后进行了基准分析，将我们的方法与以往方法进行了比较。结果表明，我们的方法在各种条件下均具有鲁棒性，且准确性和效率高，为眼科研究和临床应用提供了有价值的工具。

英文摘要：

Detecting retinal image analysis, particularly the geometrical features of branching points, plays an essential role in diagnosing eye diseases. However, existing methods used for this purpose often are coarse-level and lack fine-grained analysis for efficient annotation. To mitigate these issues, this paper proposes a novel method for detecting retinal branching angles using a self-configured image processing technique. Additionally, we offer an open-source annotation tool and a benchmark dataset comprising 40 images annotated with retinal branching angles. Our methodology for retinal branching angle detection and calculation is detailed, followed by a benchmark analysis comparing our method with previous approaches. The results indicate that our method is robust under various conditions with high accuracy and efficiency, which offers a valuable instrument for ophthalmic research and clinical applications.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。