Computed tomography (CT) plays a key role in evaluation of paranasal sinus inflammation but improved, and standardized, objective assessment is needed. Computerized volumetric analysis has benefits over visual scoring, but typically relies on manual image segmentation, which is difficult and time consuming, limiting practical applicability. We hypothesized that a convolutional neural network (CNN) algorithm can perform automatic, volumetric segmentation of the paranasal sinuses on CT enabling efficient, objective measurement of sinus opacification.
To perform initial clinical testing of a CNN for fully automatic quantitation of paranasal sinus opacification in the diagnostic workup of patients with chronic upper and lower airway disease.
Sinus CT scans were collected on 690 patients who underwent imaging as part of multidisciplinary clinical workup at a tertiary care respiratory hospital between April 2016 and November 2017. A CNN was trained to perform automatic segmentation using a subset of CTs (n = 180) that were segmented manually. A non-overlapping set (n = 510) was used for testing. CNN opacification scores were compared with Lund-MacKay (LM) visual scores, pulmonary function test results and other clinical variables using Spearman correlation and linear regression.
CNN scores were correlated with LM scores (rho = 0.82, p<0.001) and with forced expiratory volume in 1 second (FEV1) percentage predicted (rho = -0.21, p<0.001), FEV1 / forced vital capacity ratio (rho = -0.27, p<0.001), immunoglobulin E (rho = 0.20, p<0.001), eosinophil count (rho = 0.28, p<0.001) and exhaled nitric oxide (rho = 0.40, p<0.001).
Segmentation of the paranasal sinuses on CT can be automated using a CNN, providing truly objective, volumetric quantitation of sinonasal inflammation. This article is protected by copyright. All rights reserved.

This article is protected by copyright. All rights reserved.