Generalizability of a deep learning airway segmentation algorithm to a blinded low-dose CT dataset

Syed Ahmed Nadeem; Alejandro P. Comellas; Eric A. Hoffman; Punam K. Saha

doi:10.1117/12.2580224

15 February 2021 Generalizability of a deep learning airway segmentation algorithm to a blinded low-dose CT dataset

Syed Ahmed Nadeem, Alejandro P. Comellas, Eric A. Hoffman, Punam K. Saha

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115963I (2021) https://doi.org/10.1117/12.2580224
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Chronic obstructive pulmonary disease (COPD) is a common inflammatory disease characterized by restricted lung airflow affecting over 300 million people worldwide. Quantitative computed tomography (CT) has become a benchmark for large multi-center pulmonary research studies for assessment of airway and parenchymal physiology and function towards understanding the occurrence and progression of the disease. Airway tree segmentation is a precursor for such approaches; but current industry-standard methods require manual post-segmentation correction to remove leakages and add missing airway branches. Recently, deep learning (DL) methods have gained popularity in medical image segmentation and outperformed traditional image processing methods due to their data-driven optimization schemes of multi-layered and multi-scale features. Generalizability of DL methods is a lingering concern and essential in multi-site CT-based pulmonary studies due to varying CT imaging settings at different sites. In this paper, we examine the generalizability of a recently developed fully automated DL-based airway segmentation method using low-dose chest CT images from the NELSON lung cancer screening study. The DL method was trained using high-dose chest CT scans from the Iowa cohort of COPDGene study at baseline visits and applied on blinded low-dose images. Results show the recent DL-based method is generalizable to blinded low-dose chest CT imaging, and it achieves branch-level accuracies of 100, 99.6, and 96.0% at segmental, sub-segmental, and sub-sub-segmental branches along the five clinically significant bronchial paths (RB1, RB4, RB10, LB1, and LB10).

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Citation Download Citation

Syed Ahmed Nadeem, Alejandro P. Comellas, Eric A. Hoffman, and Punam K. Saha "Generalizability of a deep learning airway segmentation algorithm to a blinded low-dose CT dataset", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115963I (15 February 2021); https://doi.org/10.1117/12.2580224

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