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

Computers in Biology and Medicine, vol. 182, pp. 109192

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 8

1. Jingnan Jia Aachen Division of Image Processing Department of Radiology, Leiden University Medical Center

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   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

2. Prerak Mody Aachen Division of Image Processing Department of Radiology, Leiden University Medical Center

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   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

3. Maarten K. Ninaber Aachen Dept of Pulmonology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Pulmonology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: M.K.Ninaber@lumc.nl

4. Anne A. Schouffoer Aachen Department of Rheumatology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Rheumatology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: A.A.Schouffoer@lumc.nl

5. Jeska Kirsten De Vries-Bouwstra Aachen Department of Rheumatology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Rheumatology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: j.k.de_vries-bouwstra@lumc.nl

6. Lucia J.M. Kroft Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: L.J.M.Kroft@lumc.nl

7. Marius Staring Aachen Division of Image Processing Department of Radiology, Leiden University Medical Center

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing, Department of Radiology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: m.staring@lumc.nl

8. Berend C. Stoel corresponding Aachen Division of Image Processing Department of Radiology, Leiden University Medical Center

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing, Department of Radiology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Electronic address: b.c.stoel@lumc.nl