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. 2020 Nov;10(11):1218-1225.
doi: 10.1002/alr.22588. Epub 2020 Jun 15.

Volumetric assessment of paranasal sinus opacification on computed tomography can be automated using a convolutional neural network

Stephen M Humphries  1 , Juan Pablo Centeno  1 , Aleena M Notary  1 , Justin Gerow  1 , Giuseppe Cicchetti  2   3 , Rohit K Katial  4 , Daniel M Beswick  5 , Vijay R Ramakrishnan  5 , Rafeul Alam  4 , David A Lynch  1
Affiliations

Volumetric assessment of paranasal sinus opacification on computed tomography can be automated using a convolutional neural network

Stephen M Humphries et al. Int Forum Allergy Rhinol. 2020 Nov.

Abstract

Background: 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 could perform automatic, volumetric segmentation of the paranasal sinuses on CT, enabling efficient, objective measurement of sinus opacification. In this study we performed 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.

Methods: 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 nonoverlapping 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.

Results: CNN scores were correlated with LM scores (rho = 0.82, p < 0.001) and with forced expiratory volume in 1 second (FEV1 ) percent 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).

Conclusion: Segmentation of the paranasal sinuses on CT can be automated using a CNN, providing truly objective, volumetric quantitation of sinonasal inflammation.

Keywords: CT scan; chronic rhinosinusitis; convolutional neural network; deep learning; sinus.

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References

    1. Pallanch JF, Yu L, Delone D, et al. Three-dimensional volumetric computed tomographic scoring as an objective outcome measure for chronic rhinosinusitis: clinical correlations and comparison to Lund-Mackay scoring. Int Forum Allergy Rhinol. 2013;3:963-972.
    1. Likness MM, Pallanch JF, Sherris DA, Kita H, Mashtare TLJr, Ponikau JU. Computed tomography scans as an objective measure of disease severity in chronic rhinosinusitis. Otolaryngol Neck Surg. 2014;150:305-311.
    1. Lim S, Ramirez MV, Garneau JC, et al. Three-dimensional image analysis for staging chronic rhinosinusitis. Int Forum Allergy Rhinol. 2017;7:1052-1057.
    1. Rosenfeld RM, Piccirillo JF, Chandrasekhar SS, et al. Clinical practice guideline (update): adult sinusitis. Otolaryngol Neck Surg. 2015;152(suppl):S1-S39.
    1. Marino MJ, Weinstein JE, Riley CA, Levy JM, Emerson NA, McCoul ED. Assessment of pneumatization of the paranasal sinuses: a comprehensive and validated metric. Int Forum Allergy Rhinol. 2016;6:429-436.

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