Wang Hongyan, Lu Jie, Yang Song, Xiao Yin, He Liangliang, Dou Zhi, Zhao Wenxing, Yang Liqiang
Department of Pain Management, Xuanwu Hospital Capital Medical University, Beijing, China.
College of Information Engineering, Shanghai Maritime University, Shanghai, China.
Digit Health. 2025 Jun 2;11:20552076251347695. doi: 10.1177/20552076251347695. eCollection 2025 Jan-Dec.
Cervical spine disorders are becoming increasingly common, particularly among sedentary populations. The accurate segmentation of cervical vertebrae is critical for diagnostic and research applications. Traditional segmentation methods are limited in terms of precision and applicability across imaging modalities. The aim of this study is to develop and evaluate a fully automatic segmentation method and a user-friendly tool for detecting cervical vertebral body using a combined neural network model based on the YOLOv11 and U-Net3 + models.
A dataset of X-ray and magnetic resonance imaging (MRI) images was collected, enhanced, and annotated to include 2136 X-ray images and 2184 MRI images. The proposed YOLO-UNet ensemble model was trained and compared with four other groups of image extraction models, including YOLOv11, DeepLabV3+, U-Net3 + for direct image segmentation, and the YOLO-DeepLab network. The evaluation metrics included the Dice coefficient, Hausdorff distance, intersection over union, positive predictive value, and sensitivity.
The YOLO-UNet model combined the advantages of the YOLO and U-Net models and demonstrated excellent vertebral body segmentation capabilities on both X-ray and MRI datasets, which were closer to the ground truth images. Compared with other models, it achieved greater accuracy and a more accurate depiction of the vertebral body shape, demonstrated better versatility, and exhibited superior performance across all evaluation indicators.
The YOLO-UNet network model provided a robust and versatile solution for cervical vertebral body segmentation, demonstrating excellent accuracy and adaptability across imaging modalities on both X-ray and MRI datasets. The accompanying user-friendly tool enhanced usability, making it accessible to both clinical and research users. In this study, the challenge of large-scale medical annotation tasks was addressed, thereby reducing project costs and supporting advancements in medical information technology and clinical research.
颈椎疾病日益常见，尤其是在久坐人群中。颈椎的准确分割对于诊断和研究应用至关重要。传统的分割方法在精度和跨成像模态的适用性方面存在局限性。本研究的目的是开发并评估一种基于YOLOv11和U-Net3 +模型的组合神经网络模型，用于检测颈椎椎体的全自动分割方法和用户友好工具。
收集、增强并标注了包含2136张X射线图像和2184张磁共振成像（MRI）图像的数据集。对提出的YOLO-UNet集成模型进行训练，并与其他四组图像提取模型进行比较，包括用于直接图像分割的YOLOv11、DeepLabV3 +、U-Net3 +以及YOLO-DeepLab网络。评估指标包括Dice系数、豪斯多夫距离、交并比、阳性预测值和灵敏度。
YOLO-UNet模型结合了YOLO和U-Net模型的优点，在X射线和MRI数据集上均展现出出色的椎体分割能力，更接近真实图像。与其他模型相比，它实现了更高的准确性和对椎体形状更精确的描绘，表现出更好的通用性，并且在所有评估指标上都表现出卓越的性能。
YOLO-UNet网络模型为颈椎椎体分割提供了一种强大且通用的解决方案，在X射线和MRI数据集的跨成像模态上展现出出色的准确性和适应性。配套的用户友好工具提高了可用性，使临床和研究用户都能使用。在本研究中，解决了大规模医学标注任务的挑战，从而降低了项目成本，并支持了医学信息技术和临床研究的进步。
