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ROP lesion segmentation via sequence coding and block balancing.

Xiping Jia¹, Jianying Qiu², Dong Nie³

¹School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China.

Medical Image Analysis

|July 24, 2025

Summary

This summary is machine-generated.

A new AI model, SeBSNet, accurately detects subtle lesions in retinopathy of prematurity (ROP), a leading cause of infant blindness. This advanced segmentation network improves diagnostic accuracy for ROP, aiding timely clinical treatment.

Keywords:

Block-weighted balancing Domain knowledge coding Retinopathy of prematurity Segmentation network Sequence coding learning

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Retinopathy of prematurity (ROP) is a significant cause of vision loss in premature infants.
Accurate detection and segmentation of ROP lesions are critical for diagnosis and treatment.
Subtle and small ROP lesions present diagnostic challenges for both human experts and automated systems.

Purpose of the Study:

To develop and evaluate a novel deep learning model for improved segmentation of retinopathy of prematurity lesions.
To address the challenges posed by subtle and small ROP lesions in automated diagnostic systems.

Main Methods:

Introduction of the Sequence encoding and Block balancing-based Segmentation Network (SeBSNet).
Integration of domain knowledge coding, sequence coding learning (SCL), and block-weighted balancing (BWB) techniques.
Utilizing SeBSNet for the segmentation of retinopathy of prematurity lesions.

Main Results:

SeBSNet achieved superior performance in ROP lesion segmentation compared to state-of-the-art methods.
Achieved average ROC_AUC of 98.84%, PR_AUC of 71.90%, and Dice score of 66.88%.
Incorporating SeBSNet techniques into ROP classification networks significantly enhanced classification performance.

Conclusions:

SeBSNet offers a robust and effective solution for the automated segmentation of ROP lesions.
The proposed model holds promise for improving the diagnosis and management of retinopathy of prematurity.
The developed techniques can enhance both segmentation and classification tasks in ROP analysis.