Fully Automated Localization and Measurement of Levator Hiatus Dimensions Using 3-D Pelvic Floor Ultrasound.

Objective: To develop an algorithm for the automated localization and measurement of levator hiatus (LH) dimensions (AI-LH) using 3-D pelvic floor ultrasound.

Methods: The AI-LH included a 3-D plane regression model and a 2-D segmentation model, which first achieved automated localization of the minimal LH dimension plane (C-plane) and measurement of the hiatal area (HA) on maximum Valsalva on the rendered LH images, but not on the C-plane. The dataset included 600 volumetric data. We compared AI-LH with sonographer difference (ASD) as well as the inter-sonographer differences (IESD) in the testing dataset (n = 240). The assessment encompassed the mean absolute error (MAE) for the angle and center point distance of the C-plane, along with the Dice coefficient, MAE, and intra-class correlation coefficient (ICC) for HA, and included the time consumption.

Results: The MAE of the C-plane of ASD was 4.81 ± 2.47° with 1.92 ± 1.54 mm. AI-LH achieved a mean Dice coefficient of 0.93 for LH segmentation. The MAE on HA of ASD (1.44 ± 1.12 mm²) was lower than that of IESD (1.63 ± 1.58 mm²). The ICC on HA of ASD (0.964) was higher than that of IESD (0.949). The average time costs of AI-LH and manual measurement were 2.00 ± 0.22 s and 59.60 ± 2.63 s (t = 18.87, p < 0.01), respectively.

Conclusion: AI-LH is accurate, reliable, and robust in the localization and measurement of LH dimensions, which can shorten the time cost, simplify the operation process, and have good value in clinical applications.