Robotics and Mechatronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
Department of Obstetrics and Gynecology, University Medical Centre Utrecht, Utrecht, The Netherlands.
Ultrasound Obstet Gynecol. 2022 Oct;60(4):570-576. doi: 10.1002/uog.24810.
To develop and validate a tool for automatic selection of the slice of minimal hiatal dimensions (SMHD) and segmentation of the urogenital hiatus (UH) in transperineal ultrasound (TPUS) volumes.
Manual selection of the SMHD and segmentation of the UH was performed in TPUS volumes of 116 women with symptomatic pelvic organ prolapse (POP). These data were used to train two deep-learning algorithms. The first algorithm was trained to provide an estimation of the position of the SMHD. Based on this estimation, a slice was selected and fed into the second algorithm, which performed automatic segmentation of the UH. From this segmentation, measurements of the UH area (UHA), anteroposterior diameter (APD) and coronal diameter (CD) were computed automatically. The mean absolute distance between manually and automatically selected SMHD, the overlap (dice similarity index (DSI)) between manual and automatic UH segmentation and the intraclass correlation coefficient (ICC) between manual and automatic UH measurements were assessed on a test set of 30 TPUS volumes.
The mean absolute distance between manually and automatically selected SMHD was 0.20 cm. All DSI values between manual and automatic UH segmentations were above 0.85. The ICC values between manual and automatic UH measurements were 0.94 (95% CI, 0.87-0.97) for UHA, 0.92 (95% CI, 0.78-0.97) for APD and 0.82 (95% CI, 0.66-0.91) for CD, demonstrating excellent agreement.
Our deep-learning algorithms allowed reliable automatic selection of the SMHD and UH segmentation in TPUS volumes of women with symptomatic POP. These algorithms can be implemented in the software of TPUS machines, thus reducing clinical analysis time and simplifying the examination of TPUS data for research and clinical purposes. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
开发并验证一种用于自动选择最小食管裂孔尺寸（SMHD）切片和经会阴超声（TPUS）容积中尿生殖裂孔（UH）分割的工具。
对 116 例有症状盆腔器官脱垂（POP）的女性的 TPUS 容积进行 SMHD 的手动选择和 UH 分割。这些数据用于训练两个深度学习算法。第一个算法用于提供 SMHD 位置的估计。基于此估计，选择一个切片并输入到第二个算法中，该算法执行 UH 的自动分割。从这个分割中，自动计算 UH 区域（UHA）、前后直径（APD）和冠状直径（CD）的测量值。在 30 个 TPUS 容积的测试集中评估手动和自动选择的 SMHD 之间的平均绝对距离、手动和自动 UH 分割之间的重叠（骰子相似性指数（DSI））以及手动和自动 UH 测量之间的组内相关系数（ICC）。
手动和自动选择的 SMHD 之间的平均绝对距离为 0.20cm。手动和自动 UH 分割之间的所有 DSI 值均高于 0.85。手动和自动 UH 测量之间的 ICC 值分别为 UHA 的 0.94（95%CI，0.87-0.97）、APD 的 0.92（95%CI，0.78-0.97）和 CD 的 0.82（95%CI，0.66-0.91），表明具有极好的一致性。
我们的深度学习算法允许在有症状 POP 女性的 TPUS 容积中可靠地自动选择 SMHD 和 UH 分割。这些算法可以在 TPUS 机器的软件中实现，从而减少临床分析时间，并简化 TPUS 数据的检查，用于研究和临床目的。
