Topology-aware Mamba for Crack Segmentation in Structures
School of Computer Science and Engineering, Jiangsu University of Science and Technology（江苏科技大学计算机科学与工程学院）；School of Electronic and Informatics Engineering, Jiangsu University（江苏大学电子与信息工程学院）；School of Civil and Environmental Engineering, Oklahoma State University（俄克拉荷马州立大学土木与环境工程学院）
Xin Zuo, Yu Sheng, Jifeng Shen, Yongwei Shan
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Published at Journal of Automation in Construction
CrackMamba, a Mamba-based model, is designed for efficient and accurate crack segmentation for monitoring the structural health of infrastructure. Traditional Convolutional Neural Network (CNN) models struggle with limited receptive fields, and while Vision Transformers (ViT) improve segmentation accuracy, they are computationally intensive. CrackMamba addresses these challenges by utilizing the VMambaV2 with pre-trained ImageNet-1k weights as the encoder and a newly designed decoder for better performance. To handle the random and complex nature of crack development, a Snake Scan module is proposed to reshape crack feature sequences, enhancing feature extraction. Additionally, the three-branch Snake Conv VSS (SCVSS) block is proposed to target cracks more effectively. Experiments show that CrackMamba achieves state-of-the-art (SOTA) performance on the CrackSeg9k and SewerCrack datasets, and demonstrates competitive performance on the retinal vessel segmentation dataset CHASE\underline{~}DB1, highlighting its generalization capability. The code is publicly available at: {https://github.com/shengyu27/CrackMamba.}
Mamba-Driven Topology Fusion for Monocular 3D Human Pose Estimation
College of Sciences, Northeastern University（东北大学理学院）；Key Laboratory of Differential Equations and Their Applications, Northeastern University（东北大学偏微分方程及其应用重点实验室）；Nanjing University of Science and Technology（南京理工大学）
Graph Mamba Survival Analysis Based on Topology-Aware ordering
School of Mathematics and Statistics（数学与统计学学院）；West China Science and Technology Innovation Harbor（西部科学与技术创新港）；School of Computer Science and Technology（计算机科学与技术学院）
CrackSegFlow: Controllable Flow Matching Synthesis for Generalizable Crack Segmentation with a 50K Image-Mask Benchmark
Department of Civil and Environmental Engineering, Grainger College of Engineering, University of Illinois Urbana--Champaign（土木与环境工程系，格拉inger工程学院，伊利诺伊大学厄巴纳-香槟分校）；Department of Computer Science, Grainger College of Engineering, University of Illinois Urbana--Champaign（计算机科学系，格拉inger工程学院，伊利诺伊大学厄巴纳-香槟分校）
Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmark
Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院整形外科医院）；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Nanjing University of Science and Technology（南京理工大学）；The Second Hospital of Hebei Medical University（河北医科大学第二医院）
Topo-VM-UNetV2: Encoding Topology into Vision Mamba UNet for Polyp Segmentation
Department of Computer Science, University of Texas Rio Grande Valley（德克萨斯理工大学里奥格兰德分校计算机科学系）；Department of Mathematical Sciences, The University of Texas at Dallas（德克萨斯大学达拉斯分校数学科学系）
Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans
School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；School of Microelectronics, Shanghai University（上海大学微电子学院）；Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Eye & ENT Hospital, Fudan University（复旦大学附属眼耳鼻喉科医院）
Mamba Learns in Context: Structure-Aware Domain Generalization for Multi-Task Point Cloud Understanding
Bournemouth University（伯恩茅斯大学）；Jilin University（吉林大学）；The University of Western Australia（西澳大学）；Hangzhou City University（杭州城市大学）；Northwest A&F University（西北农林科技大学）
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Guangzhou University（广州大学）
PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation
School of Information Science and Technology, Northwest University（信息科学与技术学院，西北大学）；Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（生物医学工程研究所，工程科学系，牛津大学）
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；Helmholtz Imaging（亥姆霍兹成像中心）
