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Chau NK, Ma TT, Kim WJ, Lee CH, Jin GY, Chae KJ, Choi S. BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification. Med Biol Eng Comput 2024; 62:3107-3122. [PMID: 38777935 DOI: 10.1007/s11517-024-03119-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/04/2024] [Indexed: 05/25/2024]
Abstract
Anatomical airway labeling is crucial for precisely identifying airways displaying symptoms such as constriction, increased wall thickness, and modified branching patterns, facilitating the diagnosis and treatment of pulmonary ailments. This study introduces an innovative airway labeling methodology, BranchLabelNet, which accounts for the fractal nature of airways and inherent hierarchical branch nomenclature. In developing this methodology, branch-related parameters, including position vectors, generation levels, branch lengths, areas, perimeters, and more, are extracted from a dataset of 1000 chest computed tomography (CT) images. To effectively manage this intricate branch data, we employ an n-ary tree structure that captures the complicated relationships within the airway tree. Subsequently, we employ a divide-and-group deep learning approach for multi-label classification, streamlining the anatomical airway branch labeling process. Additionally, we address the challenge of class imbalance in the dataset by incorporating the Tomek Links algorithm to maintain model reliability and accuracy. Our proposed airway labeling method provides robust branch designations and achieves an impressive average classification accuracy of 95.94% across fivefold cross-validation. This approach is adaptable for addressing similar complexities in general multi-label classification problems within biomedical systems.
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Affiliation(s)
- Ngan-Khanh Chau
- School of Mechanical Engineering, Kyungpook National University, 80 Daehak-Ro, Buk-Gu, Daegu, 41566, Republic of Korea
- An Giang University, Vietnam National University - Ho Chi Minh City, Ho Chi Minh, Vietnam
| | - Truong-Thanh Ma
- College of Information and Communication Technology, Can Tho University, Can Tho, Vietnam
| | - Woo Jin Kim
- Department of Internal Medicine and Environmental Health Center, School of Medicine, Kangwon National University Hospital, Kangwon National University, Chuncheon, Republic of Korea
| | - Chang Hyun Lee
- Department of Radiology, College of Medicine, Seoul National University, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Gong Yong Jin
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Sanghun Choi
- School of Mechanical Engineering, Kyungpook National University, 80 Daehak-Ro, Buk-Gu, Daegu, 41566, Republic of Korea.
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Zhang ZX, Wang GY, Yang ZH, Yu X, Wang HH, Gao BJ, Zheng HT, Zhang SL, Li CL. Acoustic Signal-Based Method for Recognizing Fluid Flow States in Distillation Columns. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c02584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Zhi X. Zhang
- School of Chemical Engineering and Technology, National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, Hebei University of Technology, Tianjin300130, China
| | - Guang Y. Wang
- School of Information Engineering, Tianjin University of Commerce, Tianjin300134, China
| | - Zhen H. Yang
- School of Information Engineering, Tianjin University of Commerce, Tianjin300134, China
| | - Xiong Yu
- School of Chemical Engineering and Technology, National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, Hebei University of Technology, Tianjin300130, China
| | - Hong H. Wang
- School of Chemical Engineering and Technology, National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, Hebei University of Technology, Tianjin300130, China
| | - Bing J. Gao
- School of Chemical Engineering and Technology, National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, Hebei University of Technology, Tianjin300130, China
| | - Hao T. Zheng
- School of Chemical Engineering and Technology, National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, Hebei University of Technology, Tianjin300130, China
| | - Shu L. Zhang
- School of Chemical Engineering and Technology, National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, Hebei University of Technology, Tianjin300130, China
| | - Chun L. Li
- School of Chemical Engineering and Technology, National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, Hebei University of Technology, Tianjin300130, China
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