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SD-LayerNet: Robust and label-efficient retinal layer segmentation via anatomical priors.

Botond Fazekas¹, Guilherme Aresta¹, Dmitrii Lachinov¹

¹Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria; Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.

Computer Methods and Programs in Biomedicine

|January 14, 2025

Summary

This study presents a novel semi-supervised method for retinal layer segmentation using optical coherence tomography (OCT) scans. The approach achieves state-of-the-art performance with minimal labeled data by leveraging unlabeled data and anatomical priors.

Keywords:

Anatomical priors Deep learning Disentangled OCT Retina Semi-supervised

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Automated retinal layer segmentation in optical coherence tomography (OCT) is crucial for managing retinal diseases.
Current methods require extensive labeled data and often produce anatomically implausible results, limiting clinical utility.

Purpose of the Study:

To develop a semi-supervised method for anatomically coherent retinal layer segmentation in OCT.
To reduce reliance on large labeled datasets and improve the reliability of automated segmentation.

Main Methods:

A semi-supervised approach utilizing unlabeled OCT data and anatomical priors.
A novel topological engine for converting inferred boundaries into structured, anatomically valid segmentations.
Reconstruction of input images using disentangled representations and predicted style factors, guided by annotations and anatomical knowledge.

Main Results:

State-of-the-art performance achieved across multiple datasets, particularly in low-data regimes.
Demonstrated robustness to different acquisition settings and domain shifts.
Effective utilization of unlabeled data from different domains with minimal performance degradation.

Conclusions:

A robust and label-efficient method for retinal layer segmentation was developed.
The proposed approach significantly reduces the need for labeled training data while maintaining high performance.
The method ensures anatomical coherence and robustness, enhancing clinical applicability.