[
    {
        "id": "osp-17734",
        "type": "article-journal",
        "title": "Skeleton-Guided Progressive Test-Time Adaptation for Thin Curvilinear Structures",
        "author": [
            {
                "family": "Jang",
                "given": "Boa"
            },
            {
                "family": "Lee",
                "given": "JunGyu"
            },
            {
                "family": "Lee",
                "given": "Gwanho"
            },
            {
                "family": "Choi",
                "given": "Jinwook"
            },
            {
                "family": "Kim",
                "given": "Young-Gon"
            }
        ],
        "URL": "https://omanscience.com/en/articles/skeleton-guided-progressive-test-time-adaptation-for-thin-curvilinear-structures",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Accurate segmentation of thin curvilinear structures is vital for various real-world applications, from vessel analysis to road extraction. Yet their intricate geometry makes even minor pixel-wise errors enough to break the global topology, and this structural fragility turns severe domain shifts into catastrophic failures. The difficulty is most acute under cross-modality gaps, where the imaging process itself differs fundamentally between source and target. While test-time adaptation (TTA) offers a practical source-free remedy, existing methods adapt feature statistics and confidence, neither of which constrains connectivity, and thus degrade under such extreme gaps. To address this, we propose Skeleton-Guided Progressive Test-Time Adaptation (SGP-TTA). Progressive Batch Normalization (ProgBN) shifts normalization from frozen source statistics toward current target estimates under a sample-count schedule, so that the source-target balance follows the stage of adaptation rather than a fixed coefficient. Consensus Skeleton Recall (CSR) then derives a structural target from geometrically aligned multi-view predictions and updates only the BN affine parameters to preserve connected structures. Extensive experiments show that SGP-TTA consistently outperforms existing TTA methods in topological connectivity, with the largest margins under cross-modality shift. The project page is available at https://boa-jang.github.io/SGP-TTA."
    }
]