期刊
MATHEMATICS
卷 10, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/math10071189
关键词
artificial intelligence; semi-supervised learning; MobileNet; SENet; ResNet; three-dimensional CT; Lund-Mackay score
类别
资金
- Tri-Service General Hospital
- National Taiwan University of Science and Technology Joint Research Program [TSGH-A-111004, TCAFGH-E111044, TSGH-NTUST-111-03]
- National Defense Medical Center
- National Defense Medical Center-National Taiwan University of Science and Taichung Armed Forces General Hospital
The aim of this study is to develop an automatic segmentation algorithm based on paranasal sinus CT images, which realizes automatic identification and segmentation of the sinus boundary and its inflamed proportions, as well as the reconstruction of normal sinus and inflamed site volumes. The algorithm achieved significant improvements in both sinus segmentation and classification.
The aim of this study is to develop an automatic segmentation algorithm based on paranasal sinus CT images, which realizes automatic identification and segmentation of the sinus boundary and its inflamed proportions, as well as the reconstruction of normal sinus and inflamed site volumes. Our goal is to overcome the current clinical dilemma of manually calculating the inflammatory sinus volume, which is objective and ineffective. A semi-supervised learning algorithm using pseudo-labels for self-training was proposed to train convolutional neural networks, which consisted of SENet, MobileNet, and ResNet. An aggregate of 175 CT sets was analyzed, 50 of which were from patients who subsequently underwent sinus surgery. A 3D view and volume-based modified Lund-Mackay score were determined and compared with traditional scores. Compared to state-of-the-art networks, our modifications achieved significant improvements in both sinus segmentation and classification, with an average pixel accuracy of 99.67%, an MIoU of 89.75%, and a Dice coefficient of 90.79%. The fully automatic nasal sinus volume reconstruction system was successfully obtained the relevant detailed information by accurately acquiring the nasal sinus contour edges in the CT images. The accuracy of our algorithm has been validated and the results can be effectively applied to actual clinical medicine or forensic research.
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