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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 2024:10.1007/s11517-024-03119-7. [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] [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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Nadeem SA, Hoffman EA, Comellas AP, Saha PK. LOCALLY ADAPTIVE HALF-MAX METHODS FOR AIRWAY LUMEN-AREA AND WALL-THICKNESS AND THEIR REPEAT CT SCAN REPRODUCIBILITY. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2020; 2020:10.1109/isbi45749.2020.9098558. [PMID: 34422222 PMCID: PMC8375398 DOI: 10.1109/isbi45749.2020.9098558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Quantitative computed tomography (CT)-based characterization of bronchial metrics is increasingly being used to investigate chronic obstructive pulmonary disease (COPD)-related phenotypes. Automated methods for airway measurements benefit large multi-site studies by reducing cost and subjectivity errors. Critical challenges for CT-based analysis of airway morphology are related to location of lumen and wall transitions in the presence of varying scales and intensity-contrasts from proximal to distal sites. This paper introduces locally adaptive half-max methods to locate airway lumen and wall transitions and compute cross-sectional lumen area and wall-thickness. Also, the method uses a consistency analysis of wall-thickness to avoid adjoining-structure-artifacts. Experimental results show that computed bronchial measures at individual anatomic airway tree locations are repeat CT scan reproducible with intra-class correlation coefficient (ICC) values exceeding 0.9 and 0.8 for lumen-area and wall-thickness, respectively. Observed ICC values for derived morphologic measures, e.g., lumen-area compactness (ICC>0.67) and tapering (ICC>0.47) are relatively lower.
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Affiliation(s)
- Syed Ahmed Nadeem
- Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Eric A Hoffman
- Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Alejandro P Comellas
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Punam K Saha
- Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, IA 52242, USA
- Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
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