ROP lesion segmentation via sequence coding and block balancing

doi:10.1016/j.media.2025.103723

ROP lesion segmentation via sequence coding and block balancing

2025-07-15

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DOI：10.1016/j.media.2025.103723

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Abstract

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• We propose a network SeBSNet for segmentation of multiple types of ROP lesions. • Feature learning integrates domain knowledge via image category coding. • A block-weighted strategy boosts the lesion area importance during network training. • We labeled 350 ROP images with 4 lesion types; data to be released upon publication.

Keywords:

Block-weighted balancing

Domain knowledge coding

Retinopathy of prematurity

Segmentation network

Sequence coding learning

ROP lesion segmentation via sequence coding and block balancing

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