Chen Shaolong, Zhong Lijie, Zhang Zhiyong, Zhang Xiaodong
School of Sino-German Intelligent Manufacturing, Shenzhen City Polytechnic, Shenzhen, 518000, China.
School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou, 510000, China.
Sci Rep. 2025 Aug 19;15(1):30449. doi: 10.1038/s41598-025-16241-9.
Aiming at the difficulty of knee MRI bone and cartilage subregion segmentation caused by numerous subregions and unclear subregion boundary, a fully automatic knee subregion segmentation network based on tissue segmentation and anatomical geometry is proposed. Specifically, first, we use a transformer-based multilevel region and edge aggregation network to achieve precise segmentation of bone and cartilage tissue edges in knee MRI. Then, we designed a fibula detection module, which determines the medial and lateral of the knee by detecting the position of the fibula. Afterwards, a subregion segmentation module based on boundary information was designed, which divides bone and cartilage tissues into subregions by detecting the boundaries. In addition, in order to provide data support for the proposed model, fibula classification dataset and knee MRI bone and cartilage subregion dataset were established respectively. Testing on the fibula classification dataset we established, the proposed method achieved a detection accuracy of 1.000 in detecting the medial and lateral of the knee. On the knee MRI bone and cartilage subregion dataset we established, the proposed method attained an average dice score of 0.953 for bone subregions and 0.831 for cartilage subregions, which verifies the correctness of the proposed method.
针对膝关节MRI中骨与软骨子区域众多、子区域边界不清晰导致的子区域分割困难问题，提出了一种基于组织分割和解剖几何的膝关节子区域全自动分割网络。具体而言，首先，我们使用基于Transformer的多级区域和边缘聚合网络实现膝关节MRI中骨与软骨组织边缘的精确分割。然后，设计了一个腓骨检测模块，通过检测腓骨的位置来确定膝关节的内侧和外侧。之后，设计了一个基于边界信息的子区域分割模块，通过检测边界将骨和软骨组织划分为子区域。此外，为了为所提出的模型提供数据支持，分别建立了腓骨分类数据集和膝关节MRI骨与软骨子区域数据集。在所建立的腓骨分类数据集上进行测试，所提方法在检测膝关节内侧和外侧时的检测准确率达到1.000。在所建立的膝关节MRI骨与软骨子区域数据集上，所提方法对于骨子区域的平均Dice分数为0.953，对于软骨子区域为0.831，验证了所提方法的正确性。
