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

Asif, Zara; Tomashev, Roni; Peterkin, Veronica; Wei, Qi; Alshiek, Jonia; Baumfeld, Yael; Shobeiri, S. Abbās

doi:10.1007/s00192-022-05366-4

Cited by 2 publications

(2 citation statements)

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“…The ultrasound images acquired in a previously conducted work approved by the Institutional Ethics Committee (IRB) of the Inova Health System, Falls Church, VA, were analyzed retrospectively [ 12 ]. The 3D endovaginal ultrasound images were collected using a BK FlexFocus 3000 ultrasound machine, with a 16 MHz probe and a 360° rotational transducer.…”

Section: Methodsmentioning

confidence: 99%

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Automated Segmentation of Levator Ani Muscle from 3D Endovaginal Ultrasound Images

Rabbat,

Qureshi,

Hsu

et al. 2023

Bioengineering

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Levator ani muscle (LAM) avulsion is a common complication of vaginal childbirth and is linked to several pelvic floor disorders. Diagnosing and treating these conditions require imaging of the pelvic floor and examination of the obtained images, which is a time-consuming process subjected to operator variability. In our study, we proposed using deep learning (DL) to automate the segmentation of the LAM from 3D endovaginal ultrasound images (EVUS) to improve diagnostic accuracy and efficiency. Over one thousand images extracted from the 3D EVUS data of healthy subjects and patients with pelvic floor disorders were utilized for the automated LAM segmentation. A U-Net model was implemented, with Intersection over Union (IoU) and Dice metrics being used for model performance evaluation. The model achieved a mean Dice score of 0.86, demonstrating a better performance than existing works. The mean IoU was 0.76, indicative of a high degree of overlap between the automated and manual segmentation of the LAM. Three other models including Attention UNet, FD-UNet and Dense-UNet were also applied on the same images which showed comparable results. Our study demonstrated the feasibility and accuracy of using DL segmentation with U-Net architecture to automate LAM segmentation to reduce the time and resources required for manual segmentation of 3D EVUS images. The proposed method could become an important component in AI-based diagnostic tools, particularly in low socioeconomic regions where access to healthcare resources is limited. By improving the management of pelvic floor disorders, our approach may contribute to better patient outcomes in these underserved areas.

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Section: Methodsmentioning

confidence: 99%

“…Participants with a deficiency score less than or equal to 6 were considered to have normal LAMs, while those with a score over 6 were considered to have defective LAMs. The details about patient inclusion criteria can be found in [ 12 ]. Figure 2 shows an example of a 3D EVUS image.…”

Section: Methodsmentioning

confidence: 99%