Using 3D point cloud and graph-based neural networks to improve the estimation of pulmonary function tests from chest CT.

Journal: Computers in biology and medicine
PMID:

Abstract

Pulmonary function tests (PFTs) are important clinical metrics to measure the severity of interstitial lung disease for systemic sclerosis patients. However, PFTs cannot always be performed by spirometry if there is a risk of disease transmission or other contraindications. In addition, it is unclear how lung function is affected by changes in lung vessels. Therefore, convolution neural networks (CNNs) were previously proposed to estimate PFTs from chest CT scans (CNN-CT) and extracted vessels (CNN-Vessel). Due to GPU memory constraints, however, these networks used down-sampled images, which causes a loss of information on small vessels. Previous literature has indicated that detailed vessel information from CT scans can be helpful for PFT estimation. Therefore, this paper proposes to use a point cloud neural network (PNN-Vessel) and graph neural network (GNN-Vessel) to estimate PFTs from point cloud and graph-based representations of pulmonary vessel centerlines, respectively. After that, we combine different networks and perform multiple variable step-wise regression analysis to explore if vessel-based networks can contribute to the PFT estimation, in addition to CNN-CT. Results showed that both PNN-Vessel and GNN-Vessel outperformed CNN-Vessel, by 14% and 4%, respectively, when averaged across the intra-class correlation coefficient (ICC) scores of four PFTs metrics. In addition, compared to CNN-Vessel, PNN-Vessel used 30% of training time (1.1 h) and 7% parameters (2.1 M) and GNN-Vessel used only 7% training time (0.25 h) and 0.7% parameters (0.2 M). We combined CNN-CT, PNN-Vessel and GNN-Vessel with the weights obtained from multiple variable regression methods, which achieved the best PFT estimation accuracy (ICC of 0.748, 0.742, 0.836 and 0.835 for the four PFT measures respectively). The results verified that more detailed vessel information could provide further explanation for PFT estimation from anatomical imaging.

Authors

  • Jingnan Jia
    Division of Image Processing, Department of Radiology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: j.jia@lumc.nl.
  • Bo Yu
    Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China.
  • Prerak Mody
    Division of Image Processing, Department of Radiology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: P.P.Mody@lumc.nl.
  • Maarten K Ninaber
    Department of Pulmonology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: M.K.Ninaber@lumc.nl.
  • Anne A Schouffoer
    Department of Rheumatology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: A.A.Schouffoer@lumc.nl.
  • Jeska K de Vries-Bouwstra
    Department of Rheumatology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: j.k.de_vries-bouwstra@lumc.nl.
  • Lucia J M Kroft
    Department of Radiology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: L.J.M.Kroft@lumc.nl.
  • Marius Staring
    Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden PO Box 9600, 2300 RC, The Netherlands.
  • Berend C Stoel
    Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: b.c.stoel@lumc.nl.