Stephen M Humphries1, Juan Pablo Centeno1, Aleena M Notary1, Justin Gerow1, Giuseppe Cicchetti2,3, Rohit K Katial4, Daniel M Beswick5, Vijay R Ramakrishnan5, Rafeul Alam4, David A Lynch1. 1. Department of Radiology, National Jewish Health, Denver, CO. 2. Department of Diagnostic Imaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy. 3. Radiology Institute, Università Cattolica del Sacro Cuore, Rome, Italy. 4. Division of Allergy & Clinical Immunology, National Jewish Health, Denver, CO. 5. Department of Otolaryngology-Head and Neck Surgery, University of Colorado School of Medicine, Aurora, CO.
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.
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.
Authors: Antonio Mario Bulfamante; Francesco Ferella; Austin Michael Miller; Cecilia Rosso; Carlotta Pipolo; Emanuela Fuccillo; Giovanni Felisati; Alberto Maria Saibene Journal: Eur Arch Otorhinolaryngol Date: 2022-10-19 Impact factor: 3.236
Authors: Meredith A Jones; William M MacCuaig; Alex N Frickenstein; Seda Camalan; Metin N Gurcan; Jennifer Holter-Chakrabarty; Katherine T Morris; Molly W McNally; Kristina K Booth; Steven Carter; William E Grizzle; Lacey R McNally Journal: Biomedicines Date: 2021-02-04
Authors: Daniel M Beswick; Stephen M Humphries; Connor D Balkissoon; Matthew Strand; Eszter K Vladar; David A Lynch; Jennifer L Taylor-Cousar Journal: Ann Am Thorac Soc Date: 2022-01