Deep Learning-Based Multi-Class Segmentation of the Paranasal Sinuses of Sinusitis Patients Based on Computed Tomographic Imagesopen access
- Authors
- Whangbo, Jongwook; Lee, Juhui; Kim, Young Jae; Kim, Seon Tae; Kim, Kwang Gi
- Issue Date
- Mar-2024
- Publisher
- MDPI
- Keywords
- paranasal sinuses; chronic sinusitis; Convolutional Neural Network (CNN); multiclass segmentation
- Citation
- SENSORS, v.24, no.6
- Journal Title
- SENSORS
- Volume
- 24
- Number
- 6
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/91083
- DOI
- 10.3390/s24061933
- ISSN
- 1424-8220
1424-3210
- Abstract
- Accurate paranasal sinus segmentation is essential for reducing surgical complications through surgical guidance systems. This study introduces a multiclass Convolutional Neural Network (CNN) segmentation model by comparing four 3D U-Net variations-normal, residual, dense, and residual-dense. Data normalization and training were conducted on a 40-patient test set (20 normal, 20 abnormal) using 5-fold cross-validation. The normal 3D U-Net demonstrated superior performance with an F1 score of 84.29% on the normal test set and 79.32% on the abnormal set, exhibiting higher true positive rates for the sphenoid and maxillary sinus in both sets. Despite effective segmentation in clear sinuses, limitations were observed in mucosal inflammation. Nevertheless, the algorithm's enhanced segmentation of abnormal sinuses suggests potential clinical applications, with ongoing refinements expected for broader utility.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.