Ardelean Adriana-Ioana, Ardelean Eugen-Richard, Marginean Anca
Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Diagnostics (Basel). 2025 Jul 19;15(14):1823. doi: 10.3390/diagnostics15141823.
Optical Coherence Tomography has become a common imaging technique that enables a non-invasive and detailed visualization of the retina and allows for the identification of various diseases. Through the advancement of technology, the volume and complexity of OCT data have rendered manual analysis infeasible, creating the need for automated means of detection. This study investigates the ability of state-of-the-art object detection models, including the latest YOLO versions (from v8 to v12), YOLO-World, YOLOE, and RT-DETR, to accurately detect pathological biomarkers in two retinal OCT datasets. The AROI dataset focuses on fluid detection in Age-related Macular Degeneration, while the OCT5k dataset contains a wide range of retinal pathologies. The experiments performed show that YOLOv12 offers the best balance between detection accuracy and computational efficiency, while YOLOE manages to consistently outperform all other models across both datasets and most classes, particularly in detecting pathologies that cover a smaller area. This work provides a comprehensive benchmark of the capabilities of state-of-the-art object detection for medical applications, specifically for identifying retinal pathologies from OCT scans, offering insights and a starting point for the development of future automated solutions for analysis in a clinical setting.
光学相干断层扫描已成为一种常见的成像技术，能够对视网膜进行无创且详细的可视化，并有助于识别各种疾病。随着技术的进步，光学相干断层扫描（OCT）数据的量和复杂性使得人工分析变得不可行，从而产生了对自动检测方法的需求。本研究调查了包括最新的YOLO版本（从v8到v12）、YOLO-World、YOLOE和RT-DETR在内的先进目标检测模型在两个视网膜OCT数据集中准确检测病理生物标志物的能力。AROI数据集专注于年龄相关性黄斑变性中的液体检测，而OCT5k数据集包含广泛的视网膜病变。所进行的实验表明，YOLOv12在检测准确性和计算效率之间提供了最佳平衡，而YOLOE在两个数据集和大多数类别中始终优于所有其他模型，特别是在检测覆盖面积较小的病变方面。这项工作为医学应用中的先进目标检测能力提供了全面的基准，特别是用于从OCT扫描中识别视网膜病变，为未来临床环境中分析的自动解决方案的开发提供了见解和起点。
