Deep learning-based no-reference quality assessment of anterior segment ultrasound biomicroscopy panoramic images.

ABSTRACT:

Background

Ultrasound biomicroscopy (UBM) enables high-resolution imaging of the anterior segment, essential for accurate diagnosis and anatomical assessment. However, the quality evaluation of UBM currently relies on subjective judgment, which is time-consuming and inconsistent. This study proposes a deep learning (DL)-based no-reference method for objective and automated UBM image quality assessment (IQA), facilitating reliable selection of high-quality images for clinical use.

Methods

A total of 1,154 clinical panoramic UBM images of the anterior segment were collected from Tianjin Eye Hospital. The YOLOv8s_DW_FOCUS model was employed to accurately extract the region of interest (ROI) and identify five key anatomical landmarks: the central corneal epithelium, central corn

SUBMITTER: Miao QH 

PROVIDER: S-EPMC12591792 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature