Semi-automatic outlining of levator hiatus

Validation of an automated software (Smartpelvic™) in assessing hiatal area from three dimensional transperineal pelvic volumes of pregnant women: comparison with manual analysis

OBJECTIVES

The aim of this investigation was to evaluate the agreement between a manual and an automatic technique in assessing levator hiatus area (LHA) during pregnancy from three-dimensional (3D) pelvic floor volumes obtained by trans-perineal ultrasound (TPUS).

METHODS

3D volumes were acquired during rest, maximum pelvic floor contraction and Valsalva maneuver from 66 pregnant women. Manual selection of LHA and automatic software (Smart Pelvic™) were applied on TPUS volume starting from a C-plane view. To evaluate intra- and inter-observer variability measurements of LHA were performed twice by the same operator and once by a second sonographer. Reference hiatal contours obtained manually by the first operator were compared with the automated ones. Reproducibility was evaluated by intraclass correlation coefficients (ICC) and Bland-Altman plots.

RESULTS

LHA measurement, using automatic software, achieved excellent intra-observer and inter-observer reproducibility in pregnant women both at rest and after dynamic analysis (ICC>0.9). Further, an excellent agreement resulted between manual selection of the LHA and automatic imaging (ICC>0.9). The average time taken to obtain LHA manually was significantly longer when compared to the automatic analysis (p≤0.0001).

CONCLUSIONS

Smart pelvic software resulted from a reliable method for automatically measuring the LHA, showing high reproducibility and accuracy.

Validation of a built-in software in automatically reconstructing the tomographic images of the levator ani muscle

INTRODUCTION AND HYPOTHESIS

Transperineal ultrasound (TPUS) is an effective tool for evaluating the integrity of the levator ani muscle (LAM). Several operating steps are required to obtain the standard multi-slice image of the LAM, which is experience dependent and time consuming. This study was aimed at evaluating the feasibility and reproducibility of the built-in software, Smart-pelvic™, in reconstructing standard tomographic images of LAM from 3D/4D TPUS volumes.

METHODS

This study was conducted at a tertiary teaching hospital, enrolling women who underwent TPUS. Tomographic images of the LAM were automatically reconstructed by Smart-pelvicTM and rated by two experienced observers as standard or nonstandard. The anteroposterior diameter (APD) of the levator hiatus was also measured on the mid-sagittal plane of the automatically and manually reconstructed images. The APD measurements of each approach were compared using Bland-Altman plots, and interclass correlation coefficient (ICC) was used to evaluate intra- and inter-observer reproducibility. Meanwhile, the time taken for the reconstruction process of both methods was also recorded.

RESULTS

The ultrasound volume of a total of 104 patients were included in this study. Using Smart-pelvicTM, the overall success rate of the tomographic image reconstruction was 98%. Regarding measurements of APD, the ICC between the automatic and manual reconstruction methods was 0.99 (0.98, 0.99). The average time taken for reconstruction per case was 2.65 ± 0.52 s and 22.08 ± 3.45 s, respectively.

CONCLUSIONS

Using Smart-pelvicTM to reconstruct tomographic images of LAM is feasible, and it can promote TPUS by reducing operator dependence and improving examination efficiency in a clinical setting.

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

OBJECTIVES

To evaluate the concordance between an automatic software program and manual evaluation in reconstructing, delineating, and measuring the levator hiatus (LH) on maximal Valsalva maneuver.

METHODS

This 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.

RESULTS

The 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.

CONCLUSIONS

The 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.

Levator ani muscle volume and architecture in normal vs. muscle damage patients using 3D endovaginal ultrasound: a pilot study

INTRODUCTION AND HYPOTHESIS

This study aimed to compare the difference in levator ani muscle (LAM) volumes between 'normal' and those with sonographically visualized LAM defects. We hypothesized that the 'muscle damage' group would have a significantly lower muscle volume.

METHODS

The study included patients who had undergone a 3D endovaginal ultrasound. The normal (NM) and damage (DM) muscle groups' architectural changes were evaluated based on anterior-posterior (AP), left-right (LR) diameter, and minimal levator hiatus (MLH) area. The puboanalis-puboperinealis (PA), puborectalis (PR), and pubococcygeus-iliococcygeus (PC) were manually segmented using 2.5 vs. 1.0 mm to find the optimal sequence and to compare the volumes between NM and DM groups. POPQs were compared between the NM and DM groups.

RESULTS

The 1.0-mm segmentation volumes created superior volume analysis. Comparing NM to the DM group showed no significant difference in LAM volume. Respectively, the mean total LAM volumes were 17.27 cm³ (SD = 3.97) and 17.04 cm³ (SD = 4.32), p = 0.79. The mean MLH measurements for both groups respectively were 10.06 cm² (SD = 2.93) and 12.18 cm² (SD = 2.93), indicating a significant difference (p = 0.01). POPQ analysis demonstrated statistically significant differences at Ba and Bp parameters suggesting that the DM group had worse prolapse (p = 0.05, 0.01, respectively).

CONCLUSIONS

While LAM volumes are similar, there is a significant difference in the physical architecture of the LAM and the POPQ parameters in muscle-damaged patients compared to the normal group.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Deep learning-based pelvic levator hiatus segmentation from ultrasound images

Purpose

To automatically segment and measure the levator hiatus with a deep learning approach and evaluate the performance between algorithms, sonographers, and different devices.

Methods

Three deep learning models (UNet-ResNet34, HR-Net, and SegNet) were trained with 360 images and validated with 42 images. The trained models were tested with two test sets. The first set included 138 images to evaluate the performance between the algorithms and sonographers. An independent dataset including 679 images assessed the performances of algorithms between different ultrasound devices. Four metrics were used for evaluation: DSC, HDD, the relative error of segmentation area, and the absolute error of segmentation area.

Results

The UNet model outperformed HR-Net and SegNet. It could achieve a mean DSC of 0.964 for the first test set and 0.952 for the independent test set. UNet was creditable compared with three senior sonographers with a noninferiority test in the first test set and equivalent in the two test sets collected by different devices. On average, it took two seconds to process one case with a GPU and 2.4 s with a CPU.

Conclusions

The deep learning approach has good performance for levator hiatus segmentation and good generalization ability on independent test sets. This automatic levator hiatus segmentation approach could help shorten the clinical examination time and improve consistency.

Automatic Extraction of Hiatal Dimensions in 3-D Transperineal Pelvic Ultrasound Recordings

The aims of this work were to create a robust automatic software tool for measurement of the levator hiatal area on transperineal ultrasound (TPUS) volumes and to measure the potential reduction in variability and time taken for analysis in a clinical setting. The proposed tool automatically detects the C-plane (i.e., the plane of minimal hiatal dimensions) from a 3-D TPUS volume and subsequently uses the extracted plane to automatically segment the levator hiatus, using a convolutional neural network. The automatic pipeline was tested using 73 representative TPUS volumes. Reference hiatal outlines were obtained manually by two experts and compared with the pipeline's automated outlines. The Hausdorff distance, area, a clinical quality score, C-plane angle and C-plane Euclidean distance were used to evaluate C-plane detection and quantify levator hiatus segmentation accuracy. A visual Turing test was created to compare the performance of the software with that of the expert, based on the visual assessment of C-plane and hiatal segmentation quality. The overall time taken to extract the hiatal area with both measurement methods (i.e., manual and automatic) was measured. Each metric was calculated both for computer-observer differences and for inter-and intra-observer differences. The automatic method gave results similar to those of the expert when determining the hiatal outline from a TPUS volume. Indeed, the hiatal area measured by the algorithm and by an expert were within the intra-observer variability. Similarly, the method identified the C-plane with an accuracy of 5.76 ± 5.06° and 6.46 ± 5.18 mm in comparison to the inter-observer variability of 9.39 ± 6.21° and 8.48 ± 6.62 mm. The visual Turing test suggested that the automatic method identified the C-plane position within the TPUS volume visually as well as the expert. The average time taken to identify the C-plane and segment the hiatal area manually was 2 min and 35 ± 17 s, compared with 35 ± 4 s for the automatic result. This study presents a method for automatically measuring the levator hiatal area using artificial intelligence-based methodologies whereby the C-plane within a TPUS volume is detected and subsequently traced for the levator hiatal outline. The proposed solution was determined to be accurate, relatively quick, robust and reliable and, importantly, to reduce time and expertise required for pelvic floor disorder assessment.

Deep learning enables automatic quantitative assessment of puborectalis muscle and urogenital hiatus in plane of minimal hiatal dimensions

OBJECTIVES

To measure the length, width and area of the urogenital hiatus (UH), and the length and mean echogenicity (MEP) of the puborectalis muscle (PRM), automatically and observer-independently, in the plane of minimal hiatal dimensions on transperineal ultrasound (TPUS) images, by automatic segmentation of the UH and the PRM using deep learning.

METHODS

In 1318 three- and four-dimensional (3D/4D) TPUS volume datasets from 253 nulliparae at 12 and 36 weeks' gestation, two-dimensional (2D) images in the plane of minimal hiatal dimensions with the PRM at rest, on maximum contraction and on maximum Valsalva maneuver, were obtained manually and the UH and PRM were segmented manually. In total, 713 of the images were used to train a convolutional neural network (CNN) to segment automatically the UH and PRM in the plane of minimal hiatal dimensions. In the remainder of the dataset (test set 1 (TS1); 601 images, four having been excluded), the performance of the CNN was evaluated by comparing automatic and manual segmentations. The performance of the CNN was also tested on 117 images from an independent dataset (test set 2 (TS2); two images having been excluded) from 40 nulliparae at 12 weeks' gestation, which were acquired and segmented manually by a different observer. The success of automatic segmentation was assessed visually. Based on the CNN segmentations, the following clinically relevant parameters were measured: the length, width and area of the UH, the length of the PRM and MEP. The overlap (Dice similarity index (DSI)) and surface distance (mean absolute distance (MAD) and Hausdorff distance (HDD)) between manual and CNN segmentations were measured to investigate their similarity. For the measured clinically relevant parameters, the intraclass correlation coefficients (ICCs) between manual and CNN results were determined.

RESULTS

Fully automatic CNN segmentation was successful in 99.0% and 93.2% of images in TS1 and TS2, respectively. DSI, MAD and HDD showed good overlap and distance between manual and CNN segmentations in both test sets. This was reflected in the respective ICC values in TS1 and TS2 for the length (0.96 and 0.95), width (0.77 and 0.87) and area (0.96 and 0.91) of the UH, the length of the PRM (0.87 and 0.73) and MEP (0.95 and 0.97), which showed good to very good agreement.

CONCLUSION

Deep learning can be used to segment automatically and reliably the PRM and UH on 2D ultrasound images of the nulliparous pelvic floor in the plane of minimal hiatal dimensions. These segmentations can be used to measure reliably UH dimensions as well as PRM length and MEP. © 2018 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Automatic segmentation of puborectalis muscle on three-dimensional transperineal ultrasound

OBJECTIVES

The 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.

METHODS

The 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.

RESULTS

Interobserver 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%.

CONCLUSIONS

This 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.

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network

Segmentation of the levator hiatus in ultrasound allows the extraction of biometrics, which are of importance for pelvic floor disorder assessment. We present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a two-dimensional image extracted from a three-dimensional ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalizing activation function, which for the first time has been applied in medical imaging with CNN. SELU has important advantages such as being parameter-free and mini-batch independent, which may help to overcome memory constraints during training. A dataset with 91 images from 35 patients during Valsalva, contraction, and rest, all labeled 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 normalization. We conclude that the proposed fully automatic method achieved equivalent accuracy in segmenting the pelvic floor levator hiatus compared to a previous semiautomatic approach.