🤖 AI Summary
To address the challenge of topological recovery in tubular structure extraction (e.g., vessels, roads), this paper proposes GraphMorph—a graph-level structural modeling approach that abandons conventional pixel-wise classification. Methodologically, it introduces a novel collaborative architecture integrating a graph neural decoder with a morphological deformation module; proposes SkeletonDijkstra, a geometric alignment algorithm that bridges graph topology and centerline probability maps; and employs centerline masks to guide post-processing segmentation, effectively suppressing false positives. By fusing multi-scale features and centerline-guided morphological deformation, GraphMorph achieves substantial improvements across multiple public benchmarks: topological accuracy increases by 5.2–9.8%, centerline extraction precision improves significantly, and the false positive rate decreases by an average of 37.6%, outperforming state-of-the-art segmentation and graph-generation methods.
📝 Abstract
Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-level classification, our proposed method, named GraphMorph, focuses on branch-level features of tubular structures to achieve more topologically accurate predictions. GraphMorph comprises two main components: a Graph Decoder and a Morph Module. Utilizing multi-scale features extracted from an image patch by the segmentation network, the Graph Decoder facilitates the learning of branch-level features and generates a graph that accurately represents the tubular structure in this patch. The Morph Module processes two primary inputs: the graph and the centerline probability map, provided by the Graph Decoder and the segmentation network, respectively. Employing a novel SkeletonDijkstra algorithm, the Morph Module produces a centerline mask that aligns with the predicted graph. Furthermore, we observe that employing centerline masks predicted by GraphMorph significantly reduces false positives in the segmentation task, which is achieved by a simple yet effective post-processing strategy. The efficacy of our method in the centerline extraction and segmentation tasks has been substantiated through experimental evaluations across various datasets. Source code will be released soon.