Rostamian Reyhaneh, Panahi Masoud Shariat, Karimpour Morad, Nokiani Alireza Almasi, Khaledi Ramin Jafarzadeh, Kashani Hadi Ghattan
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Firoozabadi Clinical Research Development Unit (FACRDU), Iran University of Medical Sciences (lUMS), Tehran, Iran.
BMC Musculoskelet Disord. 2025 May 27;26(1):521. doi: 10.1186/s12891-025-08784-9.
Planning an osteotomy or arthroplasty surgery on a lower limb requires prior classification/identification of its deformities. The detection of skeletal landmarks and the calculation of angles required to identify the deformities are traditionally done manually, with measurement accuracy relying considerably on the experience of the individual doing the measurements. We propose a novel, image pyramid-based approach to skeletal landmark detection.
The proposed approach uses a Convolutional Neural Network (CNN) that receives the raw X-ray image as input and produces the coordinates of the landmarks. The landmark estimations are modified iteratively via the error feedback method to come closer to the target. Our clinically produced full-leg X-Rays dataset is made publically available and used to train and test the network. Angular quantities are calculated based on detected landmarks. Angles are then classified as lower than normal, normal or higher than normal according to predefined ranges for a normal condition.
The performance of our approach is evaluated at several levels: landmark coordinates accuracy, angles' measurement accuracy, and classification accuracy. The average absolute error (difference between automatically and manually determined coordinates) for landmarks was 0.79 ± 0.57 mm on test data, and the average absolute error (difference between automatically and manually calculated angles) for angles was 0.45 ± 0.42°.
Results from multiple case studies involving high-resolution images show that the proposed approach outperforms previous deep learning-based approaches in terms of accuracy and computational cost. It also enables the automatic detection of the lower limb misalignments in full-leg x-ray images.
计划对下肢进行截骨术或关节置换手术需要事先对其畸形进行分类/识别。传统上，骨骼标志点的检测以及识别畸形所需角度的计算是手动完成的，测量精度在很大程度上依赖于进行测量的个人经验。我们提出了一种基于图像金字塔的新颖骨骼标志点检测方法。
所提出的方法使用卷积神经网络（CNN），该网络将原始X射线图像作为输入，并生成标志点的坐标。通过误差反馈方法对标志点估计进行迭代修正，使其更接近目标。我们临床生成的全腿X射线数据集已公开可用，并用于训练和测试该网络。基于检测到的标志点计算角度量。然后根据正常情况的预定义范围将角度分类为低于正常、正常或高于正常。
我们的方法在几个层面上进行了性能评估：标志点坐标精度、角度测量精度和分类精度。在测试数据上，标志点的平均绝对误差（自动确定和手动确定的坐标之间的差异）为0.79±0.57毫米，角度的平均绝对误差（自动计算和手动计算的角度之间的差异）为0.45±0.42°。
涉及高分辨率图像的多个案例研究结果表明，所提出的方法在准确性和计算成本方面优于以前基于深度学习的方法。它还能够在全腿X射线图像中自动检测下肢排列不齐。
