Curvi-Tracker: Curvilinear structure segmentation refinement by iterative tracking

• Curvi-Tracker refines curvilinear structure segmentation using intelligent tracker agents. • Novel Direction-Net and Forward-Net improve connectivity and preserve topology. • Extensive experiments demonstrate effectiveness of the proposed Curvi-Tracker. Curvilinear structures are ubiquitous in various domains, such as blood vessels in medical images or roads in satellite images. The automation of curvilinear structure segmentation is highly beneficial because of the laborious and error-prone process of manual annotation. Existing methods produce segmentation results with decent pixel-level performance, but still with presence of incorrect connectivity. To overcome the challenge, this paper proposes Curvi-Tracker, a novel refinement framework that improves initial coarse segmentation results by deploying tracker agents on detected foreground pixels. The proposed framework has two main components: a Direction-Net and a Forward-Net, which jointly guide the movement of trackers in order to track the curvilinear object. A Direction-Aware Multi-Label loss and a Stepwise Masked loss are proposed for accurate tracking of curvilinear structures. Experiments on public datasets of various curvilinear objects including retinal vessels, roads and pavement cracks demonstrate that the proposed method consistently improves the topological correctness of coarse segmentation results coarse segmentation results, averaging overall 10 % of improvement in all three topological metrics.