Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 240900072-7.doi: 10.11896/jsjkx.240900072

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Optimization Study of Segmentation Algorithms for Lower Limb Bone on 3D CT Slices

SONG Lei1, WANG Baohui1, DU Hui2   

  1. 1 School of Software,Beihang University,Beijing 100191,China
    2 Beijing Jishuitan Hospital,Beijing 100035,China
  • Online:2025-11-15 Published:2025-11-10
  • About author:SONG Lei,born in 1995,undergra-duate.His main research interests include software engineering and compu-ter vision.
    WANG Baohui,born in 1973,senior engineer,master supervisor.His main research interests include network security,big data,artificial intelligence,etc.
  • Supported by:
    Beijing Natural Science Foundation-Haidian Original Innovation Joint Fund Project(L222059).

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Abstract: Robot-assisted lower limb osteotomy requires precise bone models for accurate pin placement and osteotomy planning.Accurate segmentation of bone tissue in CT images is essential for creating these models.This paper proposes an enhanced U-Net convolutional neural network model,which incorporates a dynamic sliding window mechanism.This mechanism adjusts the window size dynamically during the processing of sequential CT slices,improving the model’s adaptability to different cross-sectional variations and enhancing segmentation accuracy.Validation with a CT image dataset of lower limb bones from Beijing Jishuitan Hospital shows that the improved model achieves a Dice coefficient of 84.948%.This represents a significant improvement over the U-Net model(80.353%) and the Attention U-Net model(83.580%).These results highlight the effectiveness of the proposed method in achieving more accurate bone tissue segmentation.

Key words: U-Net, Medical image processing, Feature fusion, Dynamic sliding window

CLC Number: 

  • TP183
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