Conventional pixel-wise loss functions fail to enforce topological constraints in coronary vessel segmentation, producing fragmented vascular trees despite high pixel-level accuracy. We present ARIADNE, a two-stage framework coupling preference-aligned perception with RL-based diagnostic reasoning for topologically coherent stenosis detection. The perception module employs DPO to fine-tune the Sa2VA vision-language foundation model using Betti number constraints as preference signals, aligning the policy toward geometrically complete vessel structures rather than pixel-wise overlap metrics. The reasoning module formulates stenosis localization as a Markov Decision Process with an explicit rejection mechanism that autonomously defers ambiguous anatomical candidates such as bifurcations and vessel crossings, shifting from coverage maximization to reliability optimization. On 1,400 clinical angiograms, ARIADNE achieves state-of-the-art centerline Dice of 0.838, reduces false positives by 41% compared to geometric baselines. External validation on multi-center benchmarks ARCADE and XCAD confirms generalization across acquisition protocols. This represents the first application of DPO for topological alignment in medical imaging, demonstrating that preference-based learning over structural constraints mitigates topological violations while maintaining diagnostic sensitivity in interventional cardiology workflows.

翻译：传统逐像素损失函数无法约束冠脉血管分割的拓扑结构，导致尽管像素级精度很高，但生成的血管树仍存在断裂问题。我们提出ARIADNE，这是一个将偏好对齐感知与基于强化学习的诊断推理相耦合的两阶段框架，用于实现拓扑一致的狭窄检测。感知模块利用DPO，以Betti数约束作为偏好信号微调Sa2VA视觉语言基础模型，引导策略生成几何完整的血管结构而非追求像素级重叠指标。推理模块将狭窄定位建模为马尔可夫决策过程，并引入显式拒绝机制——可自主延迟处理分叉点、血管交叉等模棱两可的解剖候选区域，从而将优化目标从覆盖最大化转向可靠性优化。在1400例临床血管造影上，ARIADNE实现了0.838的最优中心线Dice系数，相比几何基线方法假阳性降低41%。在ARCADE和XCAD多中心基准数据集上的外部验证证实了其跨采集协议的泛化能力。这是DPO首次应用于医学影像拓扑对齐，证明基于结构约束的偏好学习既能减轻拓扑违反，又能保持介入心脏病工作流中的诊断敏感性。