In this paper, we propose a fully automatic method based on a densely connected convolutional network for the segmentation of the levator hiatus from ultrasound images. A densely connected path is incorporated into a U-net to achieve a deep architecture and improve the segmentation performance. The proposed network architecture provides dense connections between layers that encourage feature reuse and reduce the number of parameters while maintaining good performance. The parameters of the network are optimized by training with a binary cross entropy, i.e. logarithmic loss function. A dataset with 1000 levator hiatus images is used for training and 130 images are used for evaluating the performance of the proposed network architecture. The proposed model can get a mean Dice of [Formula: see text]. Experimental results show that the proposed method can achieve more accurate segmentation results than some of state-of-the-art methods.