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OBJECTIVES

The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.

METHODS

In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.

RESULTS

F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.

CONCLUSIONS

Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.

OBJECTIVES

METHODS

RESULTS

CONCLUSIONS

Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.

目的

本研究旨在开发一种基于 nnU-Net v2 的人工智能 (AI) 模型，用于自动分割锥形束计算机断层扫描 (CBCT) 容积中的上颌窦 (MS)，并评估该模型的性能。

方法

在 101 例 CBCT 扫描中，使用 CranioCatch 标记软件（土耳其埃斯基谢希尔）对上颌窦进行标记。数据集分为 3 部分：80 例 CBCT 扫描用于模型训练，11 例 CBCT 扫描用于模型验证，10 例 CBCT 扫描用于模型测试。使用 nnU-Net v2 深度学习模型，以 0.00001 的学习率进行 1000 个周期的模型训练。通过几个参数评估模型在 CBCT 扫描上自动分割上颌窦的性能，包括 F1 分数、准确性、灵敏度、精度、曲线下面积 (AUC)、Dice 系数 (DC)、95%Hausdorff 距离 (95%HD)和交并比 (IoU) 值。