Thin-Structure Segmentation

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12 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

TRACE: Topology-Aware Reconnection via Prediction-Adaptive Connectivity Enforcement for Thin-Structure Segmentation
Oct 15, 2026Junho Moon, Ikbeom JangThin-Structure Segmentation Image Segmentation
Hanyang University, Seoul, Republic of Korea · Hankuk University of Foreign Studies, Yongin, Republic of Korea
Skeleton-Guided Progressive Test-Time Adaptation for Thin Curvilinear Structures
Oct 8, 2026Boa Jang, JunGyu Lee, Gwanho Lee +2Image Segmentation Test-Time Adaptation
Seoul National University · Seoul National University Hospital · Yonsei University +1
Widest-Path Reachability Fields for Connectivity-Preserving Slender Structure Segmentation
Jul 8, 2026Youcheng Zong, Runda Jia, Minxuan Hu +2Image Segmentation Topology-Preserving Image Segmentation
College of Information Science and Engineering, Northeastern University, Shenyang 110004, China · Changsha Stomatological Hospital, Changsha 410005, China
From Reconstruction to Decision: A Post-Encoder Plug-in Adapter for Curvilinear Segmentation
Jun 22, 2026Qin Lei, Jiang Zhong, Xin Xiao +2Image Segmentation Thin-Structure Segmentation
Center for Big Data and Intelligent Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China · College of Computer Science, Chongqing University, Chongqing, China · Key Laboratory of Digital Health and Intelligent Medicine, Chongqing Municipal Health Commission, Chongqing, China +1
SEMIR: Topology-Preserving Graph Minors for Thin-Structure Segmentation
Jun 22, 2026Luke James Miller, Yugyung LeeImage Segmentation Thin-Structure Segmentation
University of Missouri-Kansas City, Kansas City MO 64110, USA
CSWinUNETR: Segmentation of Thin Anatomical Structures in Medical Images
Jun 18, 2026Junho Moon, Haejun Chung, Ikbeom JangThin-Structure Segmentation Hybrid CNN-Transformer Architectures
Hanyang University, Seoul, Republic of Korea · Hankuk University of Foreign Studies, Yongin, Republic of Korea
Rethinking Efficient Crack Segmentation with Task-Aligned Structural-Directional Modeling
May 29, 2026Shipeng Liu, Liang Zhao, Dengfeng Chen +1Efficient Semantic Segmentation Crack Segmentation
Xi’an University of Architecture and Technology
EDFNet: Early Fusion of Edge and Depth for Thin-Obstacle Segmentation in UAV Navigation
Apr 6, 2026Negar FathiThin-Structure Segmentation Multimodal Semantic Segmentation
University of Nebraska–Lincoln, Lincoln, NE, USA
Effect of Input Resolution on Retinal Vessel Segmentation Performance: An Empirical Study Across Five Datasets
Apr 3, 2026Amarnath RThin-Structure Segmentation Retinal Vessel Segmentation
United Institute of Technology, Coimbatore, India
Bridging the Geometry Mismatch: Frequency-Aware Anisotropic Serialization for Thin-Structure SSMs
Mar 30, 2026Jin Bai, Huiyao Zhang, Qi Wen +4Thin-Structure Segmentation Topology-Preserving Image Segmentation
University of Chinese Academy of Sciences · Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences · University of Peshawar, Pakistan +1
FMS $^2$ : Unified Flow Matching for Segmentation and Synthesis of Thin Structures
Mar 14, 2026Babak Asadi, Peiyang Wu, Mani Golparvar-Fard +2Thin-Structure Segmentation Topology-Preserving Image Segmentation
University of Illinois at Urbana-Champaign
A Boundary-Metric Evaluation Protocol for Whiteboard Stroke Segmentation Under Extreme Imbalance
Feb 26, 2026Nicholas KorcynskiImage Segmentation Thin-Structure Segmentation
Rowan University USA

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TRACE: Topology-Aware Reconnection via Prediction-Adaptive Connectivity Enforcement for Thin-Structure Segmentation

Junho Moon, Ikbeom Jang

Hanyang University, Seoul, Republic of Korea · Hankuk University of Foreign Studies, Yongin, Republic of Korea

Thin-Structure Segmentation Image Segmentation Topology-Preserving Image Segmentation Medical Image Segmentation

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Thin, elongated structures are particularly sensitive to connectivity errors, as even a few local segmentation mistakes can disrupt otherwise continuous paths. Existing skeleton-based objectives typically supervise fixed centerline locations and do not account for alternative local routes that may restore connectivity. We propose TRACE, a topology-aware, prediction-adaptive objective that uses a target skeleton to define required local connections. For each connection, TRACE identifies the easiest valid reconnection under the current prediction and jointly supervises its bottleneck with the corresponding skeleton point. The supervision thus adapts to the evolving prediction rather than remaining fixed to the centerline. We further introduce a topology-aware skeleton carrier that preserves both foreground and class-specific topology, enabling reliable definition of these connections. Extensive experiments across six 2D and 3D benchmarks show that TRACE improves connectivity and topology while maintaining strong segmentation accuracy with low training overhead. Code will be released upon acceptance.