LUMOS: Universal Semi-Supervised OCT Retinal Layer Segmentation with Hierarchical Reliable Mutual Learning

Yizhou Fang; Jian Zhong; Li Lin; Xiaoying Tang

cs.CVApr 7, 2026

LUMOS: Universal Semi-Supervised OCT Retinal Layer Segmentation with Hierarchical Reliable Mutual Learning

Authors: Yizhou Fang, Jian Zhong, Li Lin, Xiaoying Tang

Organizations: Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China · Shenzhen Loop Area Institute, Shenzhen, China · Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China

Abstract

Optical Coherence Tomography (OCT) layer segmentation faces challenges due to annotation scarcity and heterogeneous label granularities across datasets. While semi-supervised learning helps alleviate label scarcity, existing methods typically assume a fixed granularity, failing to fully exploit cross-granularity supervision. This paper presents LUMOS, a semi-supervised universal OCT retinal layer segmentation framework based on a Dual-Decoder Network with a Hierarchical Prompting Strategy (DDN-HPS) and Reliable Progressive Multi-granularity Learning (RPML). DDN-HPS combines a dual-branch architecture with a multi-granularity prompting strategy to effectively suppress pseudo-label noise propagation. Meanwhile, RPML introduces region-level reliability weighing and a progressive training approach that guides the model from easier to more difficult tasks, ensuring the reliable selection of cross-granularity consistency targets, thereby achieving stable cross-granularity alignment. Experiments on six OCT datasets demonstrate that LUMOS largely outperforms existing methods and exhibits exceptional cross-domain and cross-granularity generalization capability.

Figures & tables

Figure 1: Overview of the proposed LUMOS framework and its key components.

Figure 2: Visualization comparison results on the OIMHS, Goals, HEG and HC-MS datasets.

Setting	Internal Datasets		External Datasets		Avg.
Setting	DSC ↑	HD95 ↓	DSC ↑	HD95 ↓	DSC ↑	HD95 ↓
A1	85.13 ± 2.08	3.97 ± 9.77	82.92 ± 8.25	22.34 ± 23.04	84.03 ± 5.17	13.16 ± 16.41
A2	85.15 ± 7.38	3.98 ± 7.86	83.51 ± 7.81	16.58 ± 22.38	84.33 ± 7.60	10.28 ± 15.12

Table 3: Ablation results of the dual-branch design.

Components		Internal Datasets		External Datasets		Avg.
RLRW	MPF	DSC $\uparrow$	HD95 $\downarrow$	DSC $\uparrow$	HD95 $\downarrow$	DSC $\uparrow$	HD95 $\downarrow$
$-$	$-$	85.15 $\pm$ 7.38	3.98 $\pm$ 7.86	83.51 $\pm$ 7.81	16.58 $\pm$ 22.38	84.33 $\pm$ 7.60	10.28 $\pm$ 15.12
$\checkmark$	$-$	85.94 $\pm$ 2.31	1.98 $\pm$ 2.02	85.73 $\pm$ 9.14	11.52 $\pm$ 19.38	85.84 $\pm$ 5.73	9.28 $\pm$ 12.20
$\checkmark$	$\checkmark$	86.28 $\pm$ 7.83	1.73 $\pm$ 1.94	85.94 $\pm$ 9.14	2.99 $\pm$ 6.15	86.11 $\pm$ 8.49	2.36 $\pm$ 4.05

Table 4: Ablation results of RPML’s key components.

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PC-Seg: Progressive Cross-View Consistency for 3D OCT Segmentation from Sparse 2D Annotations

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Graduate School of Information Sciences, Tohoku University, Japan · Department of Ophthalmology, Graduate School of Medicine, Tohoku University · Institute of Ophthalmology, University College London, UK+3

Multi-View Consistency Optical Coherence Tomography Semi-Supervised Medical Image Segmentation Medical Image Segmentation 3D Medical Image Segmentation

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Volumetric segmentation of optical coherence tomography (OCT) images is essential for diagnosing ocular diseases but requires labor-intensive voxel-wise annotations. While semi-supervised learning (SSL) can reduce annotation costs, most existing methods process data slice by slice and fail to exploit the inherent 3D spatial context. We propose PC-Seg, a progressive cross-view consistency framework that learns high-accuracy 3D segmentation models from sparse 2D annotations. Unlike conventional multi-view approaches, PC-Seg uses a single 2D model to learn cross-view consistency from standard B-scans and orthogonal slices, thereby generating reliable volumetric pseudo-labels. These pseudo-labels are then distilled into a 3D model, followed by a co-training stage in which the 2D and 3D models mutually refine each other through ensemble pseudo-labeling. Experiments on the MSHC and Duke DME datasets demonstrate that PC-Seg achieves accuracy comparable to fully supervised learning while using labels for only about 0.7% of the training data, outperforming state-of-the-art semi-supervised and retinal layer segmentation methods. Our code is publicly available at https://github.com/gsisaoki/pc-seg-official.

Abstract

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