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BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification

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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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Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [grant number NRF-2023R1A2C2003781].

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Authors and Affiliations

  1. School of Mechanical Engineering, Kyungpook National University, 80 Daehak-Ro, Buk-Gu, Daegu, 41566, Republic of Korea

    Ngan-Khanh Chau & Sanghun Choi

  2. An Giang University, Vietnam National University – Ho Chi Minh City, Ho Chi Minh, Vietnam

    Ngan-Khanh Chau

  3. College of Information and Communication Technology, Can Tho University, Can Tho, Vietnam

    Truong-Thanh Ma

  4. Department of Internal Medicine and Environmental Health Center, School of Medicine, Kangwon National University Hospital, Kangwon National University, Chuncheon, Republic of Korea

    Woo Jin Kim

  5. Department of Radiology, College of Medicine, Seoul National University, Seoul National University Hospital, Seoul, Republic of Korea

    Chang Hyun Lee

  6. Department of Radiology, College of Medicine, The University of Iowa, Iowa City, IA, USA

    Chang Hyun Lee

  7. Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University–Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea

    Gong Yong Jin & Kum Ju Chae

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  1. Ngan-Khanh Chau
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Correspondence to Sanghun Choi.

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Glossary

1D

One-dimensional

BMI

Body mass index

CNN

Convolutional neural network

COPD

Chronic obstructive pulmonary disease

CT

Computed tomography

D

Diameter of a branch

Dpnode

Depth of a node in the n-ary tree

EToR

Euclidean distance from the starting point to a branch in the n-ary tree

FEV1

Forced expiratory volume in one second

FVC

Forced vital capacity

Gen

Generation of a branch

GIN

Graph isomorphic network

GNNs

Graph neural networks

isInt

Is an internal node in the n-ary tree

isLeaf

Is a leaf node in the n-ary tree

L

Length of a branch

LA

Luminal area

LMB

Left main bronchus

N child

Number of children of a node in the n-ary tree

NGCN

Nested graph convolutional network

N sib

Number of siblings of a node in the n-ary tree

P in

Inner perimeter

P out

Outer perimeter

QCT

Quantitative computed tomography

RMB

Right main bronchus

SAGE

GraphSAGE

SD

Standard deviation

SMOTE

Synthetic minority oversampling technique

SPGNN

Structure and position-aware graph neural network

TA

Total area

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Chau, NK., Ma, TT., Kim, W.J. et al. BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03119-7

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