Automatic uterus segmentation in transvaginal ultrasound using U-Net and nnU-Net.

ABSTRACT:

Purpose

Transvaginal ultrasound (TVUS) is pivotal for diagnosing reproductive pathologies in individuals assigned female at birth, often serving as the primary imaging method for gynecologic evaluation. Despite recent advancements in AI-driven segmentation, its application to gynecological ultrasound still needs further attention. Our study aims to bridge this gap by training and evaluating two state-of-the-art deep learning (DL) segmentation models on TVUS data.

Materials and methods

An experienced gynecological expert manually segmented the uterus in our TVUS dataset of 124 patients with adenomyosis, comprising still images (n = 122), video screenshots (n = 472), and 3D volume screenshots (n = 452). Two popular DL segmentation models, U-Net and nnU-Net, were trained on the

SUBMITTER: Tank D 

PROVIDER: S-EPMC12611126 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature