Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images
Merve Önder,
Cengiz Evli,
Ezgi Türk,
Orhan Kazan,
İbrahim Şevki Bayrakdar,
Özer Çelik,
Andre Luiz Ferreira Costa,
João Pedro Perez Gomes,
Celso Massahiro Ogawa,
Rohan Jagtap,
Kaan Orhan
Affiliations
Merve Önder
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06000, Turkey
Cengiz Evli
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06000, Turkey
Ezgi Türk
Dentomaxillofacial Radiology, Oral and Dental Health Center, Hatay 31040, Turkey
Orhan Kazan
Health Services Vocational School, Gazi University, Ankara 06560, Turkey
İbrahim Şevki Bayrakdar
Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir 26040, Turkey
Özer Çelik
Eskisehir Osmangazi University Center of Research and Application for Computer-Aided Diagnosis and Treatment in Health, Eskişehir 26040, Turkey
Andre Luiz Ferreira Costa
Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 01506-000, SP, Brazil
João Pedro Perez Gomes
Department of Stomatology, Division of General Pathology, School of Dentistry, University of São Paulo (USP), São Paulo 13560-970, SP, Brazil
Celso Massahiro Ogawa
Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 01506-000, SP, Brazil
Rohan Jagtap
Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS 39216, USA
Kaan Orhan
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06000, Turkey
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.
