Deep learning-based pelvic levator hiatus segmentation from ultrasound images.

ABSTRACT:

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

SUBMITTER: Huang Z 

PROVIDER: S-EPMC8956942 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature