BMSMM-Net: A Bone Metastasis Segmentation Framework Based on Mamba and Multiperspective Extraction

Metastatic bone tumors significantly reduce patients' quality of life and expedite cancer spread. Traditional diagnostic methods rely on time-consuming manual annotations by radiologists, which are prone to subjectivity. Employing deep learning for rapid, precise segmentation of bone metastases can greatly improve patient outcomes and survival. However, accurate segmentation remains challenging due to the diverse and complex nature of osteoblastic, osteolytic, or mixed lesions.

In this study, we presented a novel segmentation framework, termed BMSMM-Net, tailored specifically for the detection of bone metastases. The framework integrates our newly proposed Bottleneck Gating Mamba layer (BGM) into the network backbone, enhancing the long-range dependencies in the depth feature maps. Additionally, we designed a Skip-Mamba (SKM) module on the skip connections to facilitate long-range modeling during multi-scale feature fusion. Furthermore, a Multi-Perspective Extraction (MPE) module was employed in the feature extraction phase, utilizing three different sizes of convolutional kernels to enhance sensitivity to bone metastases.

Our framework was evaluated on the BM-Seg dataset through comparative and ablation studies. It achieved F1 scores of 91.07% and 95.17% for segmenting bone metastases and bone regions, respectively, along with mIoU scores of 83.60% and 90.78%, BMSMM-Net provides high-performance segmentation of bone metastases. Additionally, it maintains good computational efficiency compared to existing models.

The BMSMM-Net framework, integrating BGM, SKM, and MPE modules, effectively addresses the segmentation challenges of bone metastases. It significantly enhances accuracy, outperforms advanced existing methods, and maintains lower complexity, making it suitable for clinical application.