This study proposes an improved U-Net for precise segmentation of bone data to solve the problems of low contrast, indistinct features, and insufficient extraction of bone features by existing algorithms in bone computed tomography (CT) images. In the network coding stage, the densely connected dilated convolution module is used to enhance the extraction of bone features; in the network decoding stage, the attention-based fusion module is adopted to make full use of spatial information and semantic information and thereby avoid the loss of bone information. When the improved algorithm is applied to a CT dataset of human lower limb bones, the Dice coefficient is 89.44%, and the intersection over union (IoU) coefficient is 80.55%. Compared with those obtained with the U-Net model, the Dice coefficient is increased by 5.1%, and the IoU coefficient is improved by 7.63%. The experimental results show that the proposed optimization algorithm can be employed to achieve precise segmentation of CT images of lower limb bones. It also provides a reference for the preoperative planning for orthopedic diseases and subsequent treatment.