OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.
Department of Oral Medicine, Faculty of Dentistry, Mansoura University, Mansoura, Egypt.
Sci Rep. 2022 May 7;12(1):7523. doi: 10.1038/s41598-022-11483-3.
An accurate three-dimensional (3D) segmentation of the maxillary sinus is crucial for multiple diagnostic and treatment applications. Yet, it is challenging and time-consuming when manually performed on a cone-beam computed tomography (CBCT) dataset. Recently, convolutional neural networks (CNNs) have proven to provide excellent performance in the field of 3D image analysis. Hence, this study developed and validated a novel automated CNN-based methodology for the segmentation of maxillary sinus using CBCT images. A dataset of 264 sinuses were acquired from 2 CBCT devices and randomly divided into 3 subsets: training, validation, and testing. A 3D U-Net architecture CNN model was developed and compared to semi-automatic segmentation in terms of time, accuracy, and consistency. The average time was significantly reduced (p-value < 2.2e-16) by automatic segmentation (0.4 min) compared to semi-automatic segmentation (60.8 min). The model accurately identified the segmented region with a dice similarity co-efficient (DSC) of 98.4%. The inter-observer reliability for minor refinement of automatic segmentation showed an excellent DSC of 99.6%. The proposed CNN model provided a time-efficient, precise, and consistent automatic segmentation which could allow an accurate generation of 3D models for diagnosis and virtual treatment planning.
上颌窦的精确三维 (3D) 分割对于多种诊断和治疗应用至关重要。然而，当手动在上颌窦锥形束计算机断层扫描 (CBCT) 数据集上执行时，这具有挑战性且耗时。最近，卷积神经网络 (CNN) 已被证明在 3D 图像分析领域具有出色的性能。因此，本研究开发并验证了一种使用 CBCT 图像的基于新型自动化 CNN 的上颌窦分割方法。从 2 个 CBCT 设备采集了 264 个鼻窦的数据集，并随机分为 3 个子集：训练、验证和测试。开发了一个 3D U-Net 架构 CNN 模型，并在时间、准确性和一致性方面与半自动分割进行了比较。与半自动分割（60.8 分钟）相比，自动分割（0.4 分钟）的平均时间明显缩短（p 值 < 2.2e-16）。该模型准确地识别了分割区域，其骰子相似系数（DSC）为 98.4%。自动分割的次要细化的观察者间可靠性显示出极好的 DSC 为 99.6%。所提出的 CNN 模型提供了一种高效、精确和一致的自动分割方法，可允许为诊断和虚拟治疗计划准确生成 3D 模型。
