ArticleDiagnostics (Basel, Switzerland)2023
Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Real-time medical images enhancement technique using Adaptive Histogram Equalization model.Scientific reports · 2026Article
- Deploying a novel deep learning framework for segmentation of specific anatomical structures on cone-beam CT.Oral radiology · 2025Article
- MR Image Fusion-Based Parotid Gland Tumor Detection.Journal of imaging informatics in medicine · 2025Article
- Automatic deep learning detection of overhanging restorations in bitewing radiographs.Dento maxillo facial radiology · 2024Article
- Development and Validation of an Ultrasonography-Based Machine Learning Model for Predicting Outcomes of Bruxism Treatments.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.Dento maxillo facial radiology · 2024Article
- An automatic parathyroid recognition and segmentation model based on deep learning of near-infrared autofluorescence imaging.Cancer medicine · 2024Article
- A Combined Loss-driven Framework for Automated Parotid Segmentation in Head-and-Neck Computed Tomography.Journal of medical physicsArticle
- Interpretable Deep Learning Radiomics for Differentiating Pleomorphic Adenoma and Warthin Tumor.In vivo (Athens, Greece)Article
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11 authors.
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Abstract
This study aims to develop an algorithm for the automatic segmentation of the parotid gland on CT images of the head and neck using U-Net architecture and to evaluate the model's performance. In this retrospective study, a total of 30 anonymized CT volumes of the head and neck were sliced into 931 axial images of the parotid glands. Ground truth labeling was performed with the CranioCatch Annotation Tool (CranioCatch, Eskisehir, Turkey) by two oral and maxillofacial radiologists. The images were resized to 512 × 512 and split into training (80%), validation (10%), and testing (10%) subgroups. A deep convolutional neural network model was developed using U-net architecture. The automatic segmentation performance was evaluated in terms of the F1-score, precision, sensitivity, and the Area Under Curve (AUC) statistics. The threshold for a successful segmentation was determined by the intersection of over 50% of the pixels with the ground truth. The F1-score, precision, and sensitivity of the AI model in segmenting the parotid glands in the axial CT slices were found to be 1. The AUC value was 0.96. This study has shown that it is possible to use AI models based on deep learning to automatically segment the parotid gland on axial CT images.
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