Thin-Structure Segmentation

Latest papers

16 papers5 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Evaluating SAM-based and Convolutional Architectures in Thin Retinal Vessel Segmentation: Model Capacity, Computational Cost and Foundation Pre-training
Oct 30, 2026Carlos Eduardo Fontaneli, Cesar Henrique CominRetinal Vessel Segmentation Thin-Structure Segmentation
Department of Computer Science, Federal University of S˜ao Carlos, Rod. Washington Lu´ıs, km 235, 13565-905, S˜ao Carlos, SP, Brazil
Anchored Fusion for Automated Airway Segmentation in Pediatric and Adult CT
Oct 28, 2026Huseyin Umut Isik, Ege Mert Balcik, Duygu Atasoy +3Image Segmentation 3D Medical Image Segmentation
Department of Computer Engineering, METU, Ankara, Turkey · Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA · METU-DTX Digital Transformation and Innovation Center, Ankara, Turkey
DROA-CLIPSeg: Prompt-Guided Thin Crack Segmentation in Low-Light Conditions
Oct 24, 2026Vinita Shukla, Partibha Jangra, Vinayak Pathak +2Crack Segmentation Image Segmentation
Indian Institute of Technology Mandi · Indian Institute of Technology Kanpur
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
Disconnect to Connect: A Data Augmentation Method for Improving Topology Accuracy in Image Segmentation
Mar 7, 2025Juan Miguel Valverde, Maja Østergaard, Adrian Rodriguez-Palomo +4Topology-Preserving Image Segmentation Thin-Structure Segmentation
DTU Compute, Technical University of Denmark, Denmark · A.I. Virtanen Institute, University of Eastern Finland, Finland · Department of Chemistry and iNANO, Aarhus University, Denmark

Oct 30, 2026J/K move · Enter open · S save

Evaluating SAM-based and Convolutional Architectures in Thin Retinal Vessel Segmentation: Model Capacity, Computational Cost and Foundation Pre-training

Carlos Eduardo Fontaneli, Cesar Henrique Comin

Department of Computer Science, Federal University of S˜ao Carlos, Rod. Washington Lu´ıs, km 235, 13565-905, S˜ao Carlos, SP, Brazil

Retinal Vessel Segmentation Thin-Structure Segmentation Vision Transformer Vision Foundation Models

Open paper AskPDF arXiv

In retinal vessel segmentation, plain Vision Transformers (ViTs) are commonly expected to perform worse than convolutional neural networks (CNNs) on thin vessels, for reasons such as their coarse patch tokenization, their weak locality bias and their need for large training sets. Although this expectation is widely shared in the literature, how plain ViTs actually compare with CNNs on thin vessels has not been systematically quantified. We address this question by comparing a model with a plain ViT encoder (MedSAM), a hybrid Transformer (LiteMedSAM) and three CNNs of different sizes on three retinal datasets (DRIVE, HRF, FIVES) and, as a second-domain check, a microscopy dataset (VessMap). We measured the centerline recall and precision of thin vessels and compared the models while also considering their computational cost and how many epochs they needed to train. We found that the pre-trained MedSAM had a centerline recall of thin vessels that was not significantly different from that of the best CNN on all four datasets (-0.6 to +2.5 percentage points). A compact plain ViT obtained through architecture search but trained from scratch did not retain this performance. For pre-trained plain ViTs, our results do not support the expectation that they miss thin vessels, although similar thin-vessel performance can be obtained with much smaller models.