Abstract: Precise segmentation of the pelvic floor area in ultrasound images represents a critical component in the realization of computer-aided diagnosis for pelvic floor diseases. Aiming at the problems of complex pelvic floor shape, blurred boundary, huge number of segmentation algorithm parameters and limited parameter measurement accuracy, a minimizing semantic segmentation network MDL-U2-Net and patching algorithm AC-F were built forward in this research. First, the structure optimization and channel number adjustment of the benchmark U2-Net are carried out to reduce the amount of model parameters; secondly, the composite loss function is integrated to alleviate training loss fluctuations and improve the boundary retention ability, improving the segmentation accuracy of the network for blurred boundaries; following this, a Deep Asymmetric Multi-scale Shuffled Convolutional Module is introduced to capture positional offset information during feature space sampling. This module compensates for the limited receptive fields and suboptimal feature extraction capabilities inherent in lightweight networks, ultimately enhancing the network’s capacity to model the intricate structures of the pelvic floor; finally, the patching algorithm is used to refine and fill the segmented pelvic floor to improve the integrity and accuracy of parameter measurement The experimental results on the self-made dataset show that the Jaccard, Recall and HD95 indexes of MDL-U2-Net for pelvic floor segmentation reach 91.226%, 93.589% and 1.074, respectively. Compared with the benchmark U2-Net, the model parameters are reduced by 94.37 percentage points. In addition, error of the regional area measurement processed by AC-F algorithm is reduced to 1.25%, the ICC reaches 0.998 and 95%(76/80) of the data is within the 95% LoA, enabling lightweight segmentation and accurate parameter measurement.