ROP lesion segmentation via sequence coding and block balancing.

Abstract

Retinopathy of prematurity (ROP) is a potentially blinding retinal disease that often affects low birth weight premature infants. Lesion detection and recognition are crucial for ROP diagnosis and clinical treatment. However, this task poses challenges for both ophthalmologists and computer-based systems due to the small size and subtle nature of many ROP lesions. To address these challenges, we present a Sequence encoding and Block balancing-based Segmentation Network (SeBSNet), which incorporates domain knowledge coding, sequence coding learning (SCL), and block-weighted balancing (BWB) techniques into the segmentation of ROP lesions. The experimental results demonstrate that SeBSNet outperforms existing state-of-the-art methods in the segmentation of ROP lesions, with average ROC_AUC, PR_AUC, and Dice scores of 98.84%, 71.90%, and 66.88%, respectively. Furthermore, the integration of the proposed techniques into ROP classification networks as an enhancing module leads to considerable improvements in classification performance.