Learning to Trace: Zero-Shot Connectivity Repair for Vessel Segmentation via Procedural Reinforcement Learning

Structured prediction tasks such as tubular segmentation require topology preservation over long-range geometric dependencies. Pixel-wise classification methods are limited in preserving fine-scale connectivity and recovering vascular structure. Connectivity is therefore the property that matters: a segmentation broken into disconnected fragments misrepresents the network even when its pixel overlap is high. Reinforcement learning (RL) offers an alternative by explicitly tracing image geometry, but policies trained without domain adaptation may become overly sensitive to low-level image statistics and generalize poorly under distribution shift. We propose a memory-augmented actor--critic framework that traces tubular structures sequentially, using accumulated trajectory context to resolve local ambiguities. The policy jointly models exploration and branching to reconnect fragmented structures. To enable zero-shot generalization, we train the policy through an adaptive curriculum on synthesized data covering an expansive distribution of noise and geometry. When applied without adaptation to the outputs of a DRIVE-trained segmenter, the policy reduces the number of connected components ($\beta_0$) from 29.1 to 4.5 while recovering vessels missed by the segmenter, increasing centerline recall from 85.8\% to 87.7\% at 94.6\% on-vessel precision. Without retraining or modification, the same policy transfers to ISBI-12 electron-microscopy membranes, despite their distinct appearance and topology, reducing $\beta_0$ from 10.8 to 4.2 while maintaining 94.1\% recall. These results demonstrate that a sequential tracing policy learned entirely from synthetic data can improve topological coherence across substantially different imaging domains.