UniOCTSeg++: Refined Hierarchical Prompt Strategy and Bi-directional Progressive Consistency Learning for Universal Retinal Layer Segmentation in OCT.

Abstract

Universal medical image segmentation aims to unify heterogeneous datasets or annotation protocols within a single adaptable framework. However, existing prompt-based universal models often overlook background context, neglect hierarchical task dependencies, and struggle to generalize to unseen annotation granularities. These challenges are particularly pronounced in OCT-based retinal layer segmentation, where annotation schemes differ significantly across studies. To this end, we here propose UniOCTSeg++, a universal OCT segmentation framework that (1) introduces a Refined Hierarchical Prompting Strategy (RHPS) to reconstruct task-aware prompts into foreground-background paired embeddings, explicitly encoding fine-to-coarse anatomical relationships; and (2) adopts a Bi-directional Progressive Consistency Learning (BPCL) scheme that enforces mutual constraints between fine- and coarse-grained predictions under a training schedule with gradually increasing task difficulty, improving stability and mitigating pseudo-label noise. Moreover, we construct the Hierarchical Retinal OCT Segmentation Benchmark (HROCT-Bench), comprising 4.86 million OCT B-scans collected from eleven public datasets across eight annotation granularities, providing a unified evaluation protocol for universal OCT segmentation. Extensive experiments demonstrate that UniOCTSeg++ achieves state-of-the-art adaptability, reaching 90.06% DSC/ 1.38 HD95 on internal datasets and 86.83% DSC / 2.00 HD95 on external datasets. We further demonstrate UniOCTSeg++'s strong label efficiency: when trained with only 30% labeled data and supplemented with large-scale unlabeled data, UniOCTSeg++ approaches the performance of its fully supervised counterpart, highlighting its practical value for real-world deployment. The benchmark and code will be released at https://github.com/Halcyon1010/UniOCTSeg++.