Accurate segmentation of breast cells is the key link in the process o f pathological analysis of breast tissue section images,which has important va lue for the diagnosis and treatment of breast cancer.To solve the problems of u nclear cell boundary and low segmentation accuracy in breast cell image segmenta tion,an algorithm of breast cell image segmentation based on dilated U-Net net w ork is proposed.Dilated convolution is introduced into U-Net network to increa s e the receptive field of convolution layer to obtain the feature maps containing more breast cell boundary information.An instance normalization layer is added between the convolution layer and the pooling layer to improve the convergence speed of the network while alleviating the over fitting phenomenon.The weighted loss function is used to enhance the weight of breast cell region and improve t he ability of the network to extract cell features,so as to realize the effecti ve segmentation of breast cell boundaries.The experimental results on the USCB Breast dataset show that the segmentation accuracy and Dice coefficient of the p roposed algorithm are 97.63% and 83.25%,respectively,which are 6.5% and 6.6% h igher than that of the original U-Net network,and the segmentation effect is b etter for breast cell images.