Diagnostics (Feb 2023)

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

DOI
https://doi.org/10.3390/diagnostics13040581
Journal volume & issue
Vol. 13, no. 4
p. 581

Abstract

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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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