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Attention U-net for automated pulmonary fissure integrity analysis in lung computed tomography images
Journal article   Open access   Peer reviewed

Attention U-net for automated pulmonary fissure integrity analysis in lung computed tomography images

Zachary W. Althof, Sarah E. Gerard, Ali Eskandari, Mauricio S. Galizia, Eric A. Hoffman and Joseph M. Reinhardt
Scientific reports, Vol.13(1), 14135
08/29/2023
DOI: 10.1038/s41598-023-41322-y
PMCID: PMC10465516
PMID: 37644125
url
https://doi.org/10.1038/s41598-023-41322-yView
Published (Version of record) Open Access

Abstract

Computed Tomography (CT) imaging is routinely used for imaging of the lungs. Deep learning can effectively automate complex and laborious tasks in medical imaging. In this work, a deep learning technique is utilized to assess lobar fissure completeness (also known as fissure integrity) from pulmonary CT images. The human lungs are divided into five separate lobes, divided by the lobar fissures. Fissure integrity assessment is important to endobronchial valve treatment screening. Fissure integrity is known to be a biomarker of collateral ventilation between lobes impacting the efficacy of valves designed to block airflow to diseased lung regions. Fissure integrity is also likely to impact lobar sliding which has recently been shown to affect lung biomechanics. Further widescale study of fissure integrity’s impact on disease susceptibility and progression requires rapid, reproducible, and noninvasive fissure integrity assessment. In this paper we describe IntegrityNet, an attention U-Net based automatic fissure integrity analysis tool. IntegrityNet is able to predict fissure integrity with an accuracy of 95.8%, 96.1%, and 89.8% for left oblique, right oblique, and right horizontal fissures, compared to manual analysis on a dataset of 82 subjects. We also show that our method is robust to COPD severity and reproducible across subject scans acquired at different time points.

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