发表机构
The First Affiliated Hospital of Sun Yat-sen University; School of Computer Science and Technology, Hainan University; School of Mathematics and Statistics, Xi’an Jiaotong University; Chongqing University of Posts and Telecommunications（中山大学附属第一医院; 海南大学计算机科学与技术学院; 西安交通大学数学与统计学院; 重庆邮电大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对未配准的WLI/NBI内镜病灶分割问题，提出可靠性感知复域融合框架，通过拓扑正则化特征对应与可靠性引导的选择性融合，提升病灶分割性能。
AI 中文摘要
白光成像（WLI）和窄带成像（NBI）为内镜病灶提供互补视角，但因视点变化、组织变形及手持顺序采集，其配对观测常存在空间错位，导致直接WLI/NBI融合易混入非对应区域，甚至降低病灶边界处的分割性能。为解决该问题，本文提出一种用于配对但未配准的WLI/NBI病灶分割的可靠性感知复域融合框架。该框架首先建立拓扑正则化的特征对应关系，进一步估计跨模态对应可靠的区域。在该可靠性引导下，模型在可学习的复表示中选择性融合WLI和NBI特征；在该表示中，WLI衍生线索主要提供与外观相关的幅度响应，NBI衍生线索提供对结构敏感的相位响应。与传统实值或对称多模态融合不同，所提方法显式建模WLI和NBI的不同角色，并抑制局部不匹配区域中不可靠的跨模态交互。在配对WLI/NBI内镜数据集上的实验表明，所提的可靠性感知配准接地与复域融合可持续提升病灶分割性能；角色反转与模块消融研究进一步验证了模态角色设计和可靠性引导跨模态交互的必要性。
英文摘要
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.
Comments11 pages
