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

  • 0Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan Key Laboratory of Computer Technologies Application, Kunming, China (F.S., S.T., X.W., Y.L., L.W.).
Academic Radiology +

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December 1, 2024

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December 1, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces BMSMM-Net, a deep learning framework for precise bone metastasis segmentation. It significantly improves accuracy and efficiency in detecting bone metastases, aiding patient care.

Area Of Science

  • Medical imaging analysis
  • Artificial intelligence in oncology
  • Deep learning for medical diagnosis

Background

  • Metastatic bone tumors severely impact patient quality of life and cancer progression.
  • Current manual segmentation methods are time-consuming and subjective.
  • Accurate segmentation of diverse bone lesions is crucial for improved patient outcomes.

Purpose Of The Study

  • To develop a novel deep learning framework for precise and efficient segmentation of bone metastases.
  • To enhance the detection of osteoblastic, osteolytic, and mixed bone lesions.
  • To improve upon existing methods for bone metastasis segmentation.

Main Methods

  • Introduced BMSMM-Net, a novel segmentation framework for bone metastases.
  • Integrated Bottleneck Gating Mamba (BGM) and Skip-Mamba (SKM) modules to enhance feature dependency and fusion.
  • Employed a Multi-Perspective Extraction (MPE) module with varied convolutional kernels for improved sensitivity.

Main Results

  • Achieved high performance on the BM-Seg dataset with F1 scores of 91.07% for bone metastases and 95.17% for bone regions.
  • Obtained mIoU scores of 83.60% for bone metastases and 90.78% for bone regions.
  • Demonstrated superior segmentation accuracy and computational efficiency compared to existing models.

Conclusions

  • BMSMM-Net effectively addresses challenges in bone metastasis segmentation using BGM, SKM, and MPE modules.
  • The framework offers enhanced accuracy and outperforms current advanced methods.
  • BMSMM-Net's efficiency and accuracy make it suitable for clinical applications in bone metastasis detection.

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