Abstract：
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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: 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.
摘要：
目的：开发一种使用3-D盆底超声自动定位和测量提肌裂孔（LH）尺寸（AI-LH）的算法。
方法：AI-LH包括3-D平面回归模型和2-D分割模型，首先实现了最小LH尺寸平面（C平面）的自动定位，并在渲染的LH图像上最大Valsalva上测量了食道面积（HA），但不是在C-plane上.数据集包括600个体积数据。我们在测试数据集（n=240）中比较了AI-LH与超声医师差异（ASD）以及超声医师之间的差异（IESD）。评估包括C平面角度和中心点距离的平均绝对误差(MAE)，以及骰子系数，MAE,和HA的类内相关系数(ICC)，包括时间消耗。
结果：ASDC平面的MAE为4.81±2.47°，1.92±1.54mm。对于LH分割，AI-LH的平均Dice系数为0.93。ASD的HA上的MAE（1.44±1.12mm²）低于IESD的MAE（1.63±1.58mm²）。ASD的HAICC（0.964）高于IESD（0.949）。AI-LH和人工测量的平均时间成本为2.00±0.22s和59.60±2.63s(t=18.87,p<0.01),分别。
结论：AI-LH是准确的，可靠,并且在LH尺寸的定位和测量方面具有鲁棒性，这可以缩短时间成本，简化操作过程，具有良好的临床应用价值。
方法：AI-LH包括3-D平面回归模型和2-D分割模型，首先实现了最小LH尺寸平面（C平面）的自动定位，并在渲染的LH图像上最大Valsalva上测量了食道面积（HA），但不是在C-plane上.数据集包括600个体积数据。我们在测试数据集（n=240）中比较了AI-LH与超声医师差异（ASD）以及超声医师之间的差异（IESD）。评估包括C平面角度和中心点距离的平均绝对误差(MAE)，以及骰子系数，MAE,和HA的类内相关系数(ICC)，包括时间消耗。
结果：ASDC平面的MAE为4.81±2.47°，1.92±1.54mm。对于LH分割，AI-LH的平均Dice系数为0.93。ASD的HA上的MAE（1.44±1.12mm²）低于IESD的MAE（1.63±1.58mm²）。ASD的HAICC（0.964）高于IESD（0.949）。AI-LH和人工测量的平均时间成本为2.00±0.22s和59.60±2.63s(t=18.87,p<0.01),分别。
结论：AI-LH是准确的，可靠,并且在LH尺寸的定位和测量方面具有鲁棒性，这可以缩短时间成本，简化操作过程，具有良好的临床应用价值。
