Reinforcement Learning-Based Dynamic Grouping for Tubular Structure Tracking

2025-06-21

AI Summary

Tracking tubular structures—such as blood vessels and roads—is challenging under complex morphologies and environmental variations; existing segment-level methods suffer from low computational efficiency and strong reliance on elongated shape priors. Method: We formulate tubular structure tracking at the fragment level as a Markov Decision Process (MDP) and introduce Q-learning to enable dynamic path search and adaptive graph expansion: no predefined global graph topology or explicit shape prior is required; edge weights are computed online, and the search space grows incrementally on demand. Contribution/Results: Our approach significantly improves global consistency and robustness, outperforming state-of-the-art point-level and segment-level methods across mainstream tubular structure benchmarks. Notably, it maintains high structural coherence in regions with complex topologies and severe discontinuities (e.g., gaps or bifurcations), demonstrating superior generalizability and reliability.

Technology Category

• Search and Optimization: Learning to Search
• Computer Vision: Motion & Tracking
• Machine Learning: Structured Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Topic discovery and tracking

Abstract

The computation of minimal paths for the applications in tracking tubular structures such as blood vessels and roads is challenged by complex morphologies and environmental variations. Existing approaches can be roughly categorized into two research lines: the point-wise based models and the segment-wise based models. Although segment-wise approaches have obtained promising results in many scenarios, they often suffer from computational inefficiency and heavily rely on a prescribed prior to fit the target elongated shapes. We propose a novel framework that casts segment-wise tracking as a Markov Decision Process (MDP), enabling a reinforcement learning approach. Our method leverages Q-Learning to dynamically explore a graph of segments, computing edge weights on-demand and adaptively expanding the search space. This strategy avoids the high cost of a pre-computed graph and proves robust to incomplete initial information. Experimental reuslts on typical tubular structure datasets demonstrate that our method significantly outperforms state-of-the-art point-wise and segment-wise approaches. The proposed method effectively handles complex topologies and maintains global path coherence without depending on extensive prior structural knowledge.

Problem

• Tracking tubular structures with complex morphologies and variations
• Overcoming computational inefficiency in segment-wise tracking methods
• Reducing reliance on prescribed priors for elongated shape fitting

Innovation

• Reinforcement learning for dynamic segment grouping
• Q-Learning adaptively explores segment graphs
• On-demand edge weights avoid pre-computation costs