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This paper introduces a reliability-aware complex-domain fusion framework for segmenting endoscopic lesions from unregistered white-light imaging (WLI) and narrow-band imaging (NBI) data. By establishing topology-regularized feature correspondence and selectively fusing features based on reliability, the method effectively mitigates the issues of spatial misalignment and unreliable cross-modal interactions. Experimental results demonstrate significant improvements in segmentation performance, underscoring the importance of modeling the distinct roles of WLI and NBI cues in the fusion process.
Unregistered WLI/NBI fusion can be reliably achieved by selectively integrating features based on their spatial correspondence, leading to superior lesion segmentation.
White-light imaging (WLI) and narrow-band imaging (NBI) provide complementary views of endoscopic lesions, but their paired observations are often spatially misaligned due to viewpoint changes, tissue deformation, and sequential handheld acquisition. This makes direct WLI/NBI fusion prone to mixing non-corresponding regions and may even degrade segmentation around lesion boundaries. To address this problem, we propose a reliability-aware complex-domain fusion framework for paired-but-unregistered WLI/NBI lesion segmentation. The framework first establishes topology-regularized feature correspondence and further estimates where the cross-modal correspondence is reliable. Guided by this reliability, the model selectively fuses WLI and NBI features in a learnable complex representation. In this representation, WLI-derived cues mainly provide appearance-related magnitude responses, while NBI-derived cues provide structure-sensitive phase responses. Unlike conventional real-valued or symmetric multimodal fusion, the proposed method explicitly models the different roles of WLI and NBI and suppresses unreliable cross-modal interaction in locally mismatched regions. Experiments on paired WLI/NBI endoscopic datasets show that the proposed reliability-aware registration grounding and complex-domain fusion consistently improve lesion segmentation performance. Role-reversal and module ablation studies further validate the necessity of both the modality-role design and reliability-guided cross-modal interaction.
Tsinghua AI