Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.

IF 2.9 2区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

Dento maxillo facial radiology Pub Date : 2024-04-29 DOI:10.1093/dmfr/twae012

Ibrahim Sevki Bayrakdar, Nermin Sameh Elfayome, Reham Ashraf Hussien, Ibrahim Tevfik Gulsen, Alican Kuran, Ihsan Gunes, Alwaleed Al-Badr, Ozer Celik, Kaan Orhan

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引用次数: 0

Abstract

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.

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在锥形束计算机断层扫描图像上自动分割上颌窦的人工智能系统。

研究目的本研究旨在开发一种基于 nnU-Net v2 的人工智能（AI）模型，用于在锥形束计算机断层扫描（CBCT）图像中自动分割上颌窦（MS），并评估该模型的性能：数据集分为三部分：80 个 CBCT 扫描用于训练模型，11 个 CBCT 扫描用于验证模型，10 个 CBCT 扫描用于测试模型。模型训练使用 nnU-Net v2 深度学习模型，学习率为 0.00001，持续 1000 次。该模型在 CBCT 扫描中自动分割 MS 的性能通过多个参数进行评估，包括 F1 分数、准确率、灵敏度、精确度、曲线下面积（AUC）、骰子系数（DC）、95% Hausdorff 距离（95% HD）和联合交叉（IoU）值：在 CBCT 图像中成功分割上颌窦的 F1 分数、准确度、灵敏度和精确度值分别为 0.96、0.99、0.96 和 0.96。AUC、DC、95% HD、IoU 值分别为 0.97、0.96、1.19、0.93：基于 nnU-Net v2 的模型展示了在 CBCT 图像中自主、准确地分割上颌窦的能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Dento maxillo facial radiology 医学-核医学

CiteScore

5.60

自引率

9.10%

发文量

审稿时长

4-8 weeks

期刊介绍： Dentomaxillofacial Radiology (DMFR) is the journal of the International Association of Dentomaxillofacial Radiology (IADMFR) and covers the closely related fields of oral radiology and head and neck imaging. Established in 1972, DMFR is a key resource keeping dentists, radiologists and clinicians and scientists with an interest in Head and Neck imaging abreast of important research and developments in oral and maxillofacial radiology. The DMFR editorial board features a panel of international experts including Editor-in-Chief Professor Ralf Schulze. Our editorial board provide their expertise and guidance in shaping the content and direction of the journal. Quick Facts: - 2015 Impact Factor - 1.919 - Receipt to first decision - average of 3 weeks - Acceptance to online publication - average of 3 weeks - Open access option - ISSN: 0250-832X - eISSN: 1476-542X