Choi Minho, Jang Jun-Su
Digital Health Research Division, Korea Institute of Oriental Medicine, 1672 Yuseong-daero, Yuseong-gu, Daejeon, 34054, Republic of Korea.
J Imaging Inform Med. 2025 Feb;38(1):291-308. doi: 10.1007/s10278-024-01210-x. Epub 2024 Aug 5.
Medical staff inspect lumbar X-ray images to diagnose lumbar spine diseases, and the analysis process is currently automated using deep-learning techniques. The detection of landmarks is necessary in the automatic process of localizing the position and identifying the morphological features of the vertebrae. However, detection errors may occur owing to the noise and ambiguity of images, as well as individual variations in the shape of the lumbar vertebrae. This study proposes a method to improve the robustness of landmark detection results. This method assumes that landmarks are detected by a convolutional neural network-based two-step model consisting of Pose-Net and M-Net. The model generates a heatmap response to indicate the probable landmark positions. The proposed method then corrects the landmark positions using the heatmap response and active shape model, which employs statistical information on the landmark distribution. Experiments were conducted using 3600 lumbar X-ray images, and the results showed that the landmark detection error was reduced by the proposed method. The average value of maximum errors decreased by 5.58% after applying the proposed method, which combines the outstanding image analysis capabilities of deep learning with statistical shape constraints on landmark distribution. The proposed method could also be easily integrated with other techniques to increase the robustness of landmark detection results such as CoordConv layers and non-directional part affinity field. This resulted in a further enhancement in the landmark detection performance. These advantages can improve the reliability of automatic systems used to inspect lumbar X-ray images. This will benefit both patients and medical staff by reducing medical expenses and increasing diagnostic efficiency.
医务人员通过检查腰椎X线图像来诊断腰椎疾病，目前分析过程采用深度学习技术实现自动化。在自动定位椎骨位置和识别其形态特征的过程中，地标检测是必要的。然而，由于图像的噪声和模糊性，以及腰椎形状的个体差异，可能会出现检测错误。本研究提出了一种提高地标检测结果鲁棒性的方法。该方法假设地标由基于卷积神经网络的两步模型检测，该模型由姿态网络（Pose-Net）和M网络（M-Net）组成。该模型生成热图响应以指示可能的地标位置。然后，所提出的方法使用热图响应和主动形状模型来校正地标位置，主动形状模型利用地标分布的统计信息。使用3600张腰椎X线图像进行了实验，结果表明所提出的方法减少了地标检测误差。应用所提出的方法后，最大误差的平均值下降了5.58%，该方法将深度学习出色的图像分析能力与地标分布的统计形状约束相结合。所提出的方法还可以很容易地与其他技术集成，以提高地标检测结果的鲁棒性，如坐标卷积层（CoordConv layers）和无方向部分亲和场（non-directional part affinity field）。这导致地标检测性能进一步提高。这些优点可以提高用于检查腰椎X线图像的自动系统的可靠性。这将通过降低医疗费用和提高诊断效率使患者和医务人员都受益。
