A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images.

ABSTRACT:

Background

The adrenal glands are small retroperitoneal organs, few reference standards exist for adrenal CT measurements in clinical practice. This study aims to develop a deep learning (DL) model for automated adrenal gland segmentation on non-contrast CT images, and to conduct a preliminary large-scale study on age-related volume changes in normal adrenal glands using the model output values.

Methods

The model was trained and evaluated on a development dataset of annotated non-contrast CT scans of bilateral adrenal glands, utilizing nnU-Net for segmentation task. The ground truth was manually established by two experienced radiologists, and the model performance was assessed using the Dice similarity coefficient (DSC). Additionally, five radiologists provided annotations

SUBMITTER: Meng F 

PROVIDER: S-EPMC12046700 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature