"Automatic pulmonary function estimation from chest CT scans using deep regression neural networks: the relation between structure and function in systemic sclerosis"

Jingnan Jia, Emiel R. Marges, Maarten K. Ninaber, Lucia J.M. Kroft, Anne A. Schouffoer, Marius Staring and Berend C. Stoel

Abstract

Pulmonary function test (PFT) plays an important role in screening and following-up pulmonary involvement in systemic sclerosis (SSc). However, some patients are not able to perform PFT due to contraindications. In addition, it is unclear how lung function is affected by changes in lung structure in SSc. Therefore, this study aims to explore the potential of automatically estimating PFT results from chest CT scans of SSc patients and how different regions influence the estimation of PFT values. Deep regression networks were developed with transfer learning to estimate PFT from 316 SSc patients. Segmented lungs and vessels were used to mask the CT images to train the network with different inputs: from entire CT scan, lungs-only to vessels-only. The network trained by entire CT scans with transfer learning achieved an ICC of 0.71, 0.76, 0.80, and 0.81 for the estimation of DLCO, FEV1, FVC and TLC, respectively. The performance of the networks gradually decreased when trained on data from lungs-only and vessels-only. Regression attention maps showed that regions close to large vessels are highlighted more than other regions, and occasionally regions outside the lungs are highlighted. These experiments mean that apart from lungs and large vessels, other regions contribute to the estimation of PFTs. In addition, adding manually designed biomarkers increased the correlation (R) from 0.75, 0.74, 0.82, and 0.83 to 0.81, 0.83, 0.88, and 0.90, respectively. It means that that manually designed imaging biomarkers can still contribute to explaining the relation between lung function and structure.

BibTeX entry