Semi-automatic outlining of levator hiatus

Sindhwani, Nikhil; Barbosa, Daniel; Alessandrini, Martino; Heyde, Brecht; Dietz, Hans Peter; D'hooge, Jan; Deprest, Jan

doi:10.1002/uog.15777

Cited by 18 publications

(42 citation statements)

References 26 publications

(37 reference statements)

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“…Hausdorff distances and MADs are comparable with the data presented by Sindhwani et al 15 on 2D segmentation of the urogenital hiatus. The 3D-MEP ICCs are comparable with the results for 2D MEP found by Grob et al 12 .…”

Section: Discussionsupporting

confidence: 87%

“…Hausdorff distances and MADs are comparable with the data presented by Sindhwani et al . on 2D segmentation of the urogenital hiatus.…”

Section: Discussionsupporting

confidence: 87%

“…. The D ‐coefficient, however, is much lower than the values reported for 2D segmentation of the urogenital hiatus. The first explanation for this can be found, on comparison of the 2D and 3D cases, by approximating the volume of the PRM as a cylinder (V = π r 2 h) and the area of the longitudinal cross‐section cylinder plane as A = 2 r h, where r is radius and h is length.…”

Section: Discussionmentioning

confidence: 82%

“…However, in these studies parameters of the PRM were obtained manually, which is time-consuming, hindering their introduction into clinical practice. Sindhwani et al 15 proposed a method to automate the measurement of hiatal dimensions. However, these are static two-dimensional (2D) measurements, not exploiting the full potential of 3D/4D ultrasound.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automatic segmentation of puborectalis muscle on three‐dimensional transperineal ultrasound

Noort

Grob

Slump

et al. 2018

Ultrasound in Obstet & Gyne

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ObjectivesThe introduction of three‐dimensional (3D) analysis of the puborectalis muscle (PRM) for diagnostic purposes into daily practice is hindered by the need for appropriate training of observers. Automatic segmentation of the PRM on 3D transperineal ultrasound may aid its integration into clinical practice. The aims of this study were to present and assess a protocol for manual 3D segmentation of the PRM on 3D transperineal ultrasound, and to use this for training of automatic 3D segmentation method of the PRM.MethodsThe data used in this study were derived from 3D transperineal ultrasound sequences of the pelvic floor acquired at 12 weeks' gestation from nulliparous women with a singleton pregnancy. A manual 3D segmentation protocol was developed for the PRM based on a validated two‐dimensional segmentation protocol. For automatic segmentation, active appearance models of the PRM were developed, trained using manual segmentation data from 50 women. The performances of both manual and automatic segmentation were analyzed by measuring the overlap and distance between the segmentations. Intraclass correlation coefficients (ICCs) and their 95% CIs were determined for mean echogenicity and volume of the puborectalis muscle, in order to assess inter‐ and intraobserver reliabilities of the manual method using data from 20 women, as well as to compare the manual and automatic methods.ResultsInterobserver reliabilities for mean echogenicity and volume were very good for manual segmentation (ICCs 0.987 and 0.910, respectively), as were intraobserver reliabilities (ICCs 0.991 and 0.877, respectively). ICCs for mean echogenicity and volume were very good and good, respectively, for the comparison of manual vs automatic segmentation (0.968 and 0.626, respectively). The overlap and distance results for manual segmentation were as expected, showing an average mismatch of only 2–3 pixels and reasonable overlap. Based on overlap and distance, five mismatches were detected for automatic segmentation, resulting in an automatic segmentation success rate of 90%.ConclusionsThis study presents a reliable manual segmentation protocol and automatic 3D segmentation method for the PRM, which will facilitate future investigation of the PRM, allowing for the reliable measurement of potentially clinically valuable parameters such as mean echogenicity. © 2017 The Authors. Ultrasound in Obstetrics & Gynecology Published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

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Section: Discussionsupporting

confidence: 87%

“…Hausdorff distances and MADs are comparable with the data presented by Sindhwani et al . on 2D segmentation of the urogenital hiatus.…”

Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic segmentation of puborectalis muscle on three‐dimensional transperineal ultrasound

Noort

Grob

Slump

et al. 2018

Ultrasound in Obstet & Gyne

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“…Although it is true that most of the times the SP and PM defined in the 3D volume may correspond in the 2D image, this is not always the case and may need to be corrected in the axial view. Therefore, Sindhwani et al 5 method, requires to identify the two points in both images. Additionally, the contours in the C-plane rely on the manual addition of a third point and may require some additional manual adjustments.…”

Section: Introductionmentioning

confidence: 99%

Technical note: automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network

Bonmati

Sindhwani

et al. 2018

Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

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Segmentation of the levator hiatus in ultrasound allows to extract biometrics which are of importance for pelvic floor disorder assessment. In this work, we present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a 2D image extracted from a 3D ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalising activation function. SELU has important advantages such as being parameter-free and mini-batch independent. A dataset with 91 images from 35 patients all labelled by three operators, is used for training and evaluation in a leave-one-patient-out cross-validation. Results show a median Dice similarity coefficient of 0.90 with an interquartile range of 0.08, with equivalent performance to the three operators (with a Williams' index of 1.03), and outperforming a U-Net architecture without the need for batch normalisation. We conclude that the proposed fully automatic method achieved equivalent accuracy in segmenting the pelvic floor levator hiatus compared to a previous semi-automatic approach.

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Validation of an automatic method for reconstruction, delineation, and measurement of levator hiatus in clinical practice

Chen

Lin

Zhang

et al. 2023

Neurourology and Urodynamics

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ObjectivesTo evaluate the concordance between an automatic software program and manual evaluation in reconstructing, delineating, and measuring the levator hiatus (LH) on maximal Valsalva maneuver.MethodsThis was a retrospective study analyzing archived raw ultrasound imaging data of 100 patients underwent transperineal ultrasound (TPUS) examination. Each data were assessed by the automatic Smart Pelvic System software program and manual evaluation. The Dice similarity index (DSI), mean absolute distance (MAD), and Hausdorff distance (HDD) were calculated to quantify delineation accuracy of LH. Agreement between automatic and manual measurement of levator hiatus area was assessed by intraclass correlation coefficient (ICC) and Bland–Altman method.ResultsThe satisfaction rate of automatic reconstruction was 94%. Six images were recognized as unsatisfactory reconstructed images for some gas in the rectum and anal canal. Compared with satisfactory reconstructed images, DSI of unsatisfactory reconstructed images was lower, MAD and HDD were larger (p = 0.001, p = 0.001, p = 0.006, respectively). The ICC was up to 0.987 in 94 satisfactory reconstructed images.ConclusionsThe Smart Pelvic System software program had good performance in reconstruction, delineation, and measurement of LH on maximal Valsalva maneuver in clinical practice, despite misidentification of the border of posterior aspect of LH due to the influence of gas in the rectum.

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Semi-automatic outlining of levator hiatus

Abstract: Objective To create a semi-automated outlining tool for the levator hiatus, to reduce interobserver variability and and speed up analysis. Methods The proposed automated hiatus segmentation (AHS) algorithm takes a C-plane image

Cited by 18 publications

References 26 publications

Automatic segmentation of puborectalis muscle on three‐dimensional transperineal ultrasound

Automatic segmentation of puborectalis muscle on three‐dimensional transperineal ultrasound

Technical note: automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network

Validation of an automatic method for reconstruction, delineation, and measurement of levator hiatus in clinical practice

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