Önder Merve, Evli Cengiz, Türk Ezgi, Kazan Orhan, Bayrakdar İbrahim Şevki, Çelik Özer, Costa Andre Luiz Ferreira, Gomes João Pedro Perez, Ogawa Celso Massahiro, Jagtap Rohan, Orhan Kaan
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06000, Turkey.
Dentomaxillofacial Radiology, Oral and Dental Health Center, Hatay 31040, Turkey.
Diagnostics (Basel). 2023 Feb 4;13(4):581. doi: 10.3390/diagnostics13040581.
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.
本研究旨在开发一种使用U-Net架构对头颈部CT图像上的腮腺进行自动分割的算法，并评估该模型的性能。在这项回顾性研究中，共将30份匿名的头颈部CT容积切片为931张腮腺的轴向图像。由两名口腔颌面放射科医生使用CranioCatch标注工具（CranioCatch，土耳其埃斯基谢希尔）进行真值标注。将图像调整大小为512×512，并分为训练组（80%）、验证组（10%）和测试组（10%）亚组。使用U-net架构开发了一个深度卷积神经网络模型。根据F1分数、精度、灵敏度和曲线下面积（AUC）统计量评估自动分割性能。成功分割的阈值由超过50%的像素与真值的交集确定。发现AI模型在轴向CT切片中分割腮腺的F1分数、精度和灵敏度均为1。AUC值为0.96。本研究表明，使用基于深度学习的AI模型对头颈部轴向CT图像上的腮腺进行自动分割是可行的。
