Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen 91058, Germany.
Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen 91058, Germany.
Med Image Anal. 2021 May;70:102028. doi: 10.1016/j.media.2021.102028. Epub 2021 Mar 5.
Due to the lack of a standardized 3D cephalometric analysis methodology, 2D cephalograms synthesized from 3D cone-beam computed tomography (CBCT) volumes are widely used for cephalometric analysis in dental CBCT systems. However, compared with conventional X-ray film based cephalograms, such synthetic cephalograms lack image contrast and resolution, which impairs cephalometric landmark identification. In addition, the increased radiation dose applied to acquire the scan for 3D reconstruction causes potential health risks. In this work, we propose a sigmoid-based intensity transform that uses the nonlinear optical property of X-ray films to increase image contrast of synthetic cephalograms from 3D volumes. To improve image resolution, super resolution deep learning techniques are investigated. For low dose purpose, the pixel-to-pixel generative adversarial network (pix2pixGAN) is proposed for 2D cephalogram synthesis directly from two cone-beam projections. For landmark detection in the synthetic cephalograms, an efficient automatic landmark detection method using the combination of LeNet-5 and ResNet50 is proposed. Our experiments demonstrate the efficacy of pix2pixGAN in 2D cephalogram synthesis, achieving an average peak signal-to-noise ratio (PSNR) value of 33.8 with reference to the cephalograms synthesized from 3D CBCT volumes. Pix2pixGAN also achieves the best performance in super resolution, achieving an average PSNR value of 32.5 without the introduction of checkerboard or jagging artifacts. Our proposed automatic landmark detection method achieves 86.7% successful detection rate in the 2 mm clinical acceptable range on the ISBI Test1 data, which is comparable to the state-of-the-art methods. The method trained on conventional cephalograms can be directly applied to landmark detection in the synthetic cephalograms, achieving 93.0% and 80.7% successful detection rate in 4 mm precision range for synthetic cephalograms from 3D volumes and 2D projections, respectively.
由于缺乏标准化的三维头影测量分析方法，二维头影测量图像通常由三维锥形束 CT（CBCT）容积重建合成，广泛应用于牙科 CBCT 系统中的头影测量分析。然而，与传统的基于 X 射线胶片的头影测量图像相比，这种合成的头影测量图像缺乏图像对比度和分辨率，这会影响头影测量标志点的识别。此外，为了进行三维重建而增加的扫描辐射剂量会带来潜在的健康风险。在这项工作中，我们提出了一种基于 Sigmoid 的强度变换方法，利用 X 射线胶片的非线性光学特性来提高三维容积重建的合成头影测量图像的对比度。为了提高图像分辨率，我们研究了超分辨率深度学习技术。为了实现低剂量扫描的目的，我们提出了一种基于像素到像素生成对抗网络（pix2pixGAN）的二维头影测量图像直接从两个锥形束投影重建的方法。对于合成头影测量图像中的标志点检测，我们提出了一种基于 LeNet-5 和 ResNet50 的高效自动标志点检测方法。我们的实验证明了 pix2pixGAN 在二维头影测量图像重建中的有效性，与从三维 CBCT 容积重建的头影测量图像相比，平均峰值信噪比（PSNR）值达到 33.8。pix2pixGAN 在超分辨率方面也取得了最佳性能，平均 PSNR 值为 32.5，没有出现棋盘格或锯齿状伪影。我们提出的自动标志点检测方法在 ISBI Test1 数据的 2mm 临床可接受范围内的成功率达到了 86.7%，与最先进的方法相当。在传统头影测量图像上训练的方法可以直接应用于合成头影测量图像中的标志点检测，在 3D 容积重建和二维投影的合成头影测量图像中，分别达到了 93.0%和 80.7%的成功率。
