通过在最少标注 X 光片上进行鲁棒斑块提取实现脊柱骨赘检测
Spinal Osteophyte Detection via Robust Patch Extraction on minimally annotated X-rays
AI总结：
针对脊柱 X 光片中微小骨赘检测难题，本文提出结合深度学习椎骨分割与掩膜轮廓扩大的 SegPatch 自动斑块提取方法，在少量标注下显著优于基线。
摘要：
关节炎的发展和进展与骨赘密切相关，骨赘是一种微小且难以察觉的骨质增生。本文介绍了在脊柱 X 光片中自动检测脊柱骨赘的最早探索之一。基于深度学习驱动的椎骨分割和掩膜轮廓扩大，研究提出了一种名为 SegPatch 的新型自动化斑块提取流程。该方法最终取得了 84.5% 的斑块分类准确率，比基于分块的基线斑块生成技术高出 9.5%。这表明，即使标注有限，SegPatch 仍能在检测骨赘等微小结构时提供卓越性能。所提方法有望协助临床医生加快在脊柱 X 光片中人工识别骨赘的过程。
英文摘要：
The development and progression of arthritis is strongly associated with osteophytes, which are small and elusive bone growths. This paper presents one of the first efforts towards automated spinal osteophyte detection in spinal X-rays. A novel automated patch extraction process, called SegPatch, has been proposed based on deep learning-driven vertebrae segmentation and the enlargement of mask contours. A final patch classification accuracy of 84.5\% is secured, surpassing a baseline tiling-based patch generation technique by 9.5%. This demonstrates that even with limited annotations, SegPatch can deliver superior performance for detection of tiny structures such as osteophytes. The proposed approach has potential to assist clinicians in expediting the process of manually identifying osteophytes in spinal X-ray.
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。
