Aiming at the problems of artifact existing in activated sludge phase contrast microscopic images and low segmentation accuracy of existing image segmentation methods for filamentous bacteria, a segmentation model of an activated sludge microscopic image based on the U-Net network, residual network, channel attention mechanism, and atrous spatial pyramid module is proposed. The ResNet network based on channel attention mechanism is used as an encoder. Channel attention mechanism explicitly establishes the dependence among feature channels, and analyzes the feature extraction ability of the residual network reinforcement model. At the end of the encoder, the atrous spatial pyramid pooling is added, which can obtain the multi-scale information of filaments and flocs without increasing the parameters. In order to enhance the ability of network reconstruction, the feature information is supplemented by using jump connection in the decoder. Experimental results show that the proposed model has better segmentation performance and effect than U-Net and DeepLabV3+.