Brown adipose tissue (BAT) is a kind of adipose tissue engaging in thermoregulatory thermogenesis, metaboloregulatory thermogenesis, and secretory. Current studies have revealed that BAT activity is negatively correlated with adult body weight and is considered a target tissue for the treatment of obesity and other metabolic-related diseases. Additionally, the activity of BAT presents certain differences between different ages and genders. Clinically, BAT segmentation based on PET/CT data is a reliable method for brown fat research. However, most of the current BAT segmentation methods rely on the experience of doctors. In this paper, an improved U-net network, ICA-Unet, is proposed to achieve automatic and precise segmentation of BAT. First, the traditional 2D convolution layer in the encoder is replaced with a depth-wise overparameterized convolutional (Do-Conv) layer. Second, the channel attention block is introduced between the double-layer convolution. Finally, the image information entropy (IIE) block is added in the skip connections to strengthen the edge features. Furthermore, the performance of this method is evaluated on the dataset of PET/CT images from 368 patients. The results demonstrate a strong agreement between the automatic segmentation of BAT and manual annotation by experts. The average DICE coe±cient (DSC) is 0.9057, and the average Hausdorff distance is 7.2810. Experimental results suggest that the method proposed in this paper can achieve e±cient and accurate automatic BAT segmentation and satisfy the clinical requirements of BAT.