In nuclear medicine,single-photon emission computed tomography (SPECT) bone imaging is an important means to assist physicians in diagnosing diseases.Aiming at the problems of low signal-to-noise ratio,blurred boundaries,small lesions,and time-consuming manual lesion delineation in bone imaging images,an automatic segmentation algorithm for bone imaging lesions based on improved U-Net network was proposed.Based on the original convolution block of U-Net,the algorithm adopts a multi-scale dense connection (MDC) method to improve the extraction ability of small lesion features,and at the same time solves the problem of gradient disappearance after the network is deepened.Second,to extract detailed features of lesions,an attention mechanism structure is introduced at dense and skip connections.Finally,in view of the problem that the model is difficult to converge when using a small sample dataset,the transfer learning method is used to optimize the initial parameters of the model and improve the generalization ability and segmentation efficiency of the model.In addition,in order to reduce the amount of computation and further improve the segmentation effect,the dataset is cropped and denoised.At the same time,the processed images are augmented by rotation,mirroring and other methods.The experimental results show that the improved U-Net′s recognition precision and mean intersection-over-union ratio (mIoU) can reach 0.735 〖ＫＧ－１／６〗2 and 0.467 3,respectively,which are better than the current mainstream segmentation algorithms,and have certain practical application value.