Dynamic Exploration on Segment-Proposal Graphs for Tubular Centerline Tracking

TL;DR

This paper introduces a dynamic, on-demand graph construction method for tubular centerline tracking that improves accuracy and efficiency by adaptively expanding the graph during search, addressing limitations of static graph approaches.

Contribution

It proposes a novel Q-learning based dynamic exploration scheme for segment-proposal graph construction, enabling on-the-fly graph expansion during path search.

Findings

Improved accuracy over state-of-the-art methods.

Enhanced efficiency in tubular centerline tracking.

Effective on diverse datasets like retinal vessels, roads, and rivers.

Abstract

Optimal curve methods provide a fundamental framework for tubular centerline tracking. Point-wise approaches, such as minimal paths, are theoretically elegant but often suffer from shortcut and short-branch combination problems in complex scenarios. Nonlocal segment-wise methods address these issues by mapping pre-extracted centerline fragments onto a segment-proposal graph, performing optimization in this abstract space, and recovering the target tubular centerline from the resulting optimal path. In this paradigm, graph construction is critical, as it directly determines the quality of the final result. However, existing segment-wise methods construct graphs in a static manner, requiring all edges and their weights to be pre-computed, i.e. the graph must be sufficiently complete prior to search. Otherwise, the true path may be absent from the candidate space, leading to search failure. To address this limitation, we propose a dynamic exploration scheme for constructing segment-proposal graphs, where the graph is built on demand during the search for optimal paths. By formulating the problem as a Markov decision process, we apply Q-learning to compute edge weights only for visited transitions and adaptively expand the action space when connectivity is insufficient. Experimental results on retinal vessels, roads, and rivers demonstrate consistent improvements over state-of-the-art methods in both accuracy and efficiency.