Abstract: There are higher demands for the performance and effectiveness of segmentation algorithms in the domain of medical image segmentation,due to disturbances suchas noise,artifacts,and low contrast in lower limb bone CT images.In response to this demand,a tailored improvement of the image segmentation model based on the U-Net convolutional neural network model and the characteristics of three-dimensional CT image input data is proposed,improving the accuracy of segmentation.The proposed model,which is based on the U-Net module,is employing multiple layers of convolutional pooling aggregation,combined with attention mechanisms and feature fusion between consecutive slices.This approach can fully explore the features and structural information in the image,achieving an end-to-end image segmentation method.The paper validates the model using a dataset of lower limb bone CT images from Xishan Hospital.Experimental results demonstrate that the average intersection over union(IoU) of the proposed model reaches 84.959%,while the corresponding value of other models is 78.604%(U-Net),80.481%(Nested U-Net),and 79.877%(Attention U-Net),respectively.The proposed model shows significant improvements compared to other models.

Key words: Convolutional neural network, Image segmentation, U-Net, Medical image processing, Feature fusion, Attention mecha-nisms