Wang Xin, Fu Yupeng, Lu Huimin, Xia Yuchen, Cai Xiaodong
College of Computer Science and Engineering, Changchun University of Technology, Changchun, China.
Information Department, Jilin Qianwei Hospital, Changchun, China.
PLoS One. 2025 Aug 28;20(8):e0330740. doi: 10.1371/journal.pone.0330740. eCollection 2025.
In medical imaging diagnosis, accurate segmentation of the knee joint can help doctors better observe and diagnose lesions, thereby improving diagnostic accuracy and treatment effectiveness. Vision Mamba mainly relies on the State Space Model (SSM) for feature modeling, which excels at capturing global contextual information but cannot capture local texture features. Moreover, features of different scales are not effectively integrated, resulting in the model's weak segmentation ability on small-scale tissues (such as cartilage areas). To this end, this study proposed a novel multi-scale Vision Mamba Unet (VM-Unet) framework named MSPF-VM-Unet to perform the segmentation on the femur, tibia, femoral cartilage, and tibial cartilage in knee MRI images. The proposed MSPF-VM-Unet extends VM-Unet by introducing a designed multi-scale pyramid feature extraction network named MPSK, which synergizes multi-resolution feature extraction with channel-space attention. MPSK network enhances multi-scale local feature extraction through Selective Kernel (SK) convolution and pyramid pooling. The network merges the overall context information extracted by the Vision Mamba encoder to achieve the coordinated optimization of a multi-scale hierarchical feature fusion mechanism and global long-range dependency modeling. The results of the comparative experiments on the OAI-ZIB dataset indicate that MSPF-VM-Unet significantly improves the boundary accuracy and regional consistency of the MRI tibiofemoral joint tissue structure.
在医学影像诊断中，膝关节的精确分割有助于医生更好地观察和诊断病变，从而提高诊断准确性和治疗效果。视觉曼巴主要依靠状态空间模型（SSM）进行特征建模，该模型擅长捕捉全局上下文信息，但无法捕捉局部纹理特征。此外，不同尺度的特征没有得到有效整合，导致模型对小尺度组织（如软骨区域）的分割能力较弱。为此，本研究提出了一种名为MSPF-VM-Unet的新型多尺度视觉曼巴Unet（VM-Unet）框架，用于对膝关节MRI图像中的股骨、胫骨、股骨软骨和胫骨软骨进行分割。所提出的MSPF-VM-Unet通过引入一个名为MPSK的设计多尺度金字塔特征提取网络来扩展VM-Unet，该网络将多分辨率特征提取与通道空间注意力相结合。MPSK网络通过选择性内核（SK）卷积和金字塔池化增强多尺度局部特征提取。该网络融合了视觉曼巴编码器提取的整体上下文信息，以实现多尺度分层特征融合机制和全局长程依赖建模的协同优化。在OAI-ZIB数据集上的对比实验结果表明，MSPF-VM-Unet显著提高了MRI胫股关节组织结构的边界准确性和区域一致性。
