Deep learning-based pelvic levator hiatus segmentation from ultrasound images.

Purpose: To automatically segment and measure the levator hiatus with a deep learning approach and evaluate the performance between algorithms, sonographers, and different devices. Methods: Three deep learning models (UNet-ResNet34, HR-Net, and SegNet) were trained with 360 images and validated with 42 images. The trained models were tested with two test sets. The first set included 138 images to evaluate the performance between the algorithms and sonographers. An independent dataset including 679 images assessed the performances of algorithms between different ultrasound devices. Four metrics were used for evaluation: DSC, HDD, the relative error of segmentation area, and the absolute error of segmentation area. Results: The UNet model outperformed HR-Net and SegNet. It could achieve a mean DSC of 0.964 for the first test set and 0.952 for the independent test set. UNet was creditable compared with three senior sonographers with a noninferiority test in the first test set and equivalent in the two test sets collected by different devices. On average, it took two seconds to process one case with a GPU and 2.4 s with a CPU. Conclusions: The deep learning approach has good performance for levator hiatus segmentation and good generalization ability on independent test sets. This automatic levator hiatus segmentation approach could help shorten the clinical examination time and improve consistency.