Automatic identification and segmentation of the slice of minimal hiatal dimensions in transperineal ultrasound volumes.

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Abstract

Automatic selection and segmentation of the slice of minimal hiatal dimensions (SMHD) in transperineal ultrasound (TPUS) volumes.The SMHD was manually selected and the urogenital hiatus (UH) segmented in TPUS volumes of 116 women with symptomatic pelvic organ prolapse (POP). These data were used to train two deep learning algorithms: the first one provides an estimation of the position of the SMHD. Based on this estimation a slice is selected and fed into the second algorithm, which automatically segments the UH. From this segmentation measurements of hiatal area (HA), anteroposterior (APD) and coronal (CD) diameter are computed. 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 testset of 30 TPUS volumes.The mean absolute distance between manually and automatically selected SMHD was 0.20 cm. DSI values between manual and automatic segmentation were all above 0.85. The ICC values and 95% confidence interval between manual and automatic levator hiatus measurements were 0.94 (0.87-0.97) for levator HA, 0.92 (0.78-0.97) for APD and 0.82 (0.66-0.91) for CD.Our deep learning algorithms allow for reliable automatic selection and segmentation of the SMHD 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 easing the examination of TPUS data for research or clinical purposes. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.