🤖 AI Summary
To address the high cost of pixel-level annotations and low-quality pseudo-labels in weakly supervised OCT image segmentation, this paper proposes a dual-guided (structural and textual) pseudo-label generation framework. Methodologically: (1) a structure-aware layer enhancement module is designed to improve anatomical layer segmentation robustness; (2) a dual-path text-guided mechanism integrates image-level label-derived textual descriptions with synthetically generated descriptive texts to achieve vision–semantics cross-modal alignment; (3) the framework incorporates CLIP-driven cross-domain text embeddings, a dual-branch visual encoder, and an iterative pseudo-label refinement strategy. Evaluated on three public OCT datasets, the method achieves significant mIoU improvements over existing weakly supervised approaches, establishing new state-of-the-art performance. The source code and pretrained models are publicly released.
📝 Abstract
Accurate segmentation of Optical Coherence Tomography (OCT) images is crucial for diagnosing and monitoring retinal diseases. However, the labor-intensive nature of pixel-level annotation limits the scalability of supervised learning for large datasets. Weakly Supervised Semantic Segmentation (WSSS) offers a promising alternative by using weaker forms of supervision, such as image-level labels, to reduce the annotation burden. Despite its advantages, weak supervision inherently carries limited information. We propose a novel WSSS framework with only image-level labels for OCT lesion segmentation that integrates structural and text-driven guidance to produce high-quality, pixel-level pseudo labels. The framework employs two visual processing modules: one that processes the original OCT images and another that operates on layer segmentations augmented with anomalous signals, enabling the model to associate lesions with their corresponding anatomical layers. Complementing these visual cues, we leverage large-scale pretrained models to provide two forms of textual guidance: label-derived descriptions that encode local semantics, and domain-agnostic synthetic descriptions that, although expressed in natural image terms, capture spatial and relational semantics useful for generating globally consistent representations. By fusing these visual and textual features in a multi-modal framework, our method aligns semantic meaning with structural relevance, thereby improving lesion localization and segmentation performance. Experiments on three OCT datasets demonstrate state-of-the-art results, highlighting its potential to advance diagnostic accuracy and efficiency in medical imaging. The code and pretrained models are publicly available at https://github.com/YangjiaqiDig/WSSS-AGM/tree/master/structure_guided.