对比4种深度学习模型，高效定位上肢骨骼关节，提升医学影像诊断准确率。
A Comparative Study of Multiple Deep Learning Algorithms for Efficient Localization of Bone Joints in the Upper Limbs of Human Body
- 采用YOLOv3、YOLOv7、EfficientDet和CenterNet四类模型进行关节检测
- YOLOv7在mAP（0.5:0.95）达48.3%，精度最优
- 适合医学影像自动化分析与放射科辅助诊断使用
本文研究人体上肢（肘、肩、腕、指关节）的骨关节定位问题。从X射线和计算机断层扫描（CT）中自动定位关节是评估骨关节炎、类风湿性关节炎等疾病的关键步骤，也可用于骨折的自动检测。该方法可同时识别对应骨骼，作为深度学习模型的输入，提升疾病诊断准确性，减轻放射科医生的分析负担。本研究对YOLOv3、YOLOv7、EfficientDet和CenterNet四种深度学习模型进行了详尽比较，基于公开的MURA数据集进行训练与测试。结果显示，各模型在mAP（0.5:0.95）上的表现分别为：YOLOv3为35.3，YOLOv7为48.3，EfficientDet为46.5，CenterNet为45.9。此外，视觉分析显示，YOLOv7在边界框预测上最精准，而YOLOv3表现最差。代码已开源。
原文摘要 · Abstract (English)
This paper addresses the medical imaging problem of joint detection in the upper limbs, viz. elbow, shoulder, wrist and finger joints. Localization of joints from X-Ray and Computerized Tomography (CT) scans is an essential step for the assessment of various bone-related medical conditions like Osteoarthritis, Rheumatoid Arthritis, and can even be used for automated bone fracture detection. Automated joint localization also detects the corresponding bones and can serve as input to deep learning-based models used for the computerized diagnosis of the aforementioned medical disorders. This in-creases the accuracy of prediction and aids the radiologists with analyzing the scans, which is quite a complex and exhausting task. This paper provides a detailed comparative study between diverse Deep Learning (DL) models - YOLOv3, YOLOv7, EfficientDet and CenterNet in multiple bone joint detections in the upper limbs of the human body. The research analyses the performance of different DL models, mathematically, graphically and visually. These models are trained and tested on a portion of the openly available MURA (musculoskeletal radiographs) dataset. The study found that the best Mean Average Precision (mAP at 0.5:0.95) values of YOLOv3, YOLOv7, EfficientDet and CenterNet are 35.3, 48.3, 46.5 and 45.9 respectively. Besides, it has been found YOLOv7 performed the best for accurately predicting the bounding boxes while YOLOv3 performed the worst in the Visual Analysis test. Code available at https://github.com/Sohambasu07/BoneJointsLocalization
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