一种通用的贝塞尔树编码反事实框架用于视网膜血管介导的疾病分析
A General Bézier Tree Encoding Counterfactual Framework for Retinal-Vessel-Mediated Disease Analysis
AI总结：
本文提出BTECF框架，通过贝塞尔曲线抽象血管网络，实现结构拓扑的显式保留与可扰动，结合扩散生成器进行参数级反事实干预，验证了其在糖尿病视网膜病变、脑梗死和阿尔茨海默病中的有效性。
摘要：
视网膜血管的几何结构是血管疾病的生物标志物，但临床证据仍主要为观察性研究。现有生成反事实方法仅在图像级疾病标签上进行干预，无法隔离显式解剖结构。为此，我们提出贝塞尔树编码反事实框架（BTECF）。通过将血管网络抽象为相互连接的三次贝塞尔段，BTECF建立了一种疾病无关的表示，在其中显式保留结构拓扑并可原子级扰动。将此编码与基于扩散的生成器结合，可以在显式几何轴（如 tortuosity、caliber）上进行参数级 do-干预，同时保持背景视网膜纹理。我们验证了BTECF在糖尿病视网膜病变上的有效性，并独立队列用于脑梗死和阿尔茨海默病。孤立的反事实干预产生剂量响应式的分类器预测变化；匹配的像素丢弃对照使这种响应减少一个数量级或更多，排除了分布外生成伪影。通过强制隔离血管拓扑与像素级混杂因素之间的因果关系，BTECF提供了一个统一的生成范式，用于在系统性疾病中的假设验证。为支持可重复性，接受时将公开代码。
英文摘要：
The geometry of the retinal vessel is a key biomarker of vascular diseases, yet clinical evidence remains primarily observational. Existing generative counterfactuals intervene only at the image-level disease label, failing to isolate explicit anatomical structure. To address this limitation, we propose the Bézier Tree Encoding Counterfactual Framework (BTECF). By abstracting vascular networks into interconnected cubic-Bézier segments, BTECF establishes a disease-agnostic representation in which structural topology is explicitly preserved and atomically perturbable. Coupling this encoding with a diffusion-based generator enables parameter-level do-interventions on explicit geometric axes (e.g., tortuosity, caliber) while preserving background fundus textures. We validate BTECF on diabetic retinopathy, together with independent cohorts for ischemic stroke and Alzheimer's disease. Isolated counterfactual interventions produce dose-responsive shifts in classifier predictions; a matched pixel-drop control attenuates this response by an order of magnitude or more, ruling out out-of-distribution generation artifacts. By enforcing causal isolation between vessel topology and pixel-level confounders, BTECF provides a unified generative paradigm for hypothesis verification across systemic diseases. To support reproducibility, the code will be publicly released upon acceptance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
