Minimizing segmentation and parameter measurement of pelvic floor ultrasound images based on MDL-U2-Net network

Aiming at the problems of changing pelvic floor shape, blurred edges and limited computing power of existing end point equipment in ultrasonic images, a lightweight semantic segmentation algorithm DML-U2-Net is proposed. For the problem of low pelvic floor parameter measurement accuracy, AC-F is proposed. The repair method is refined. Firstly, the structure optimization and channel number adjustment of the benchmark U2-Net are carried out, which effectively reduces the amount of network parameters, and an interlayer weighted hybrid loss function based on Log-SD loss is proposed to alleviate the loss fluctuation during the network training process, so as to improve the network. The boundary retention ability of the network solves the problem of low edge segmentation accuracy caused by the blurred boundary of the pelvic floor; secondly, the DAMS convolutional module is introduced to replace part of the single convolutional layer in the network to make up for the shortcomings of the small network receptive field and weak feature extraction ability, and improve the network's adaptability to the morphological pelvic floor target. Finally, the area and contour of the pelvic floor are refined by the AC-F repair method to improve the integrity of the pelvic floor and the accuracy of parameter measurement. The experimental results show that the Jaccard, Recall and HD95 indexes of pelvic floor ultrasonic segmentation by DML-U2-Net reach 91.226%, 93.589% and 1.074, respectively, and the model parameters are reduced by 94.37% to 11.4M; in addition, the RA measurement after AC-F treatment The percentage error is smaller, 1.25%, ICC reaches 0.998 and 95% (76/80) of the data is within the 95% LoA range, which can realize lightweight segmentation and accurate parameter measurement.