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