The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses

Chronic rhinosinusitis (CRS) is diagnosed with symptoms and objective endoscopy or computed tomography (CT). The Lund-Mackay score (LMS) is often used to determine the radiologic severity of CRS and make clinical decisions. This proof-of-concept study aimed to develop an automated algorithm combining a convolutional neural network (CNN) for sinus segmentation with post-processing to compute LMS directly from CT scans.

Radiology Information System was queried for outpatient paranasal sinus CTs at a tertiary institution. We identified 1,399 CT scans which were manually labelled with LMS of individual sinuses. Seventy-seven CT scans with 13,668 coronal images were segmented manually for individual sinuses. Our model for segmentation achieved a mean Dice score of 0.85 for all sinus regions, except for the osteomeatal complex. For individual Dice scores were 0.95, 0.71, 0.78, 0.93, 0.86 for the maxillary, anterior ethmoid, posterior ethmoid, sphenoid, and frontal sinuses, respectively. LMS was computed automatically by applying adaptive image thresholding and pixel counting to the CNN's segmented regions. A convolutional neural network (CNN) model was trained to segment each sinus region. Overall, the LMS model showed a high degree of accuracy with a score of 0.92, 0.99, 0.99, 0.97, 0.99, 0.86 for the maxillary, anterior ethmoid, posterior ethmoid, sphenoid, and frontal sinuses, respectively.

Reporting of paranasal sinus CT can be automated and potentially standardized with a CNN model to provide accurate Lund-Mackay score.