Human Tooth Crack Image Analysis with Multiple Deep Learning Approaches.

IF 3 2区 医学 Q3 ENGINEERING, BIOMEDICAL
Zheng Li, Zhongqiang Li, Ya Zhang, Huaizhi Wang, Xin Li, Jian Zhang, Waleed Zaid, Shaomian Yao, Jian Xu
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Abstract

Tooth cracks, one of the most common dental diseases, can result in the tooth falling apart without prompt treatment; dentists also have difficulty locating cracks, even with X-ray imaging. Indocyanine green (ICG) assisted near-infrared fluorescence (NIRF) dental imaging technique can solve this problem due to the deep penetration of NIR light and the excellent fluorescence characteristics of ICG. This study extracted 593 human cracked tooth images and 601 non-cracked tooth images from NIR imaging videos. Multiple imaging analysis methods such as classification, object detection, and super-resolution were applied to the dataset for cracked image analysis. Our results showed that machine learning methods could help analyze tooth crack efficiently: the tooth images with cracks and without cracks could be well classified with the pre-trained residual network and squeezenet1_1 models, with a classification accuracy of 88.2% and 94.25%, respectively; the single shot multi-box detector (SSD) was able to recognize cracks, even if the input image was at a different size from the original cracked image; the super-resolution (SR) model, SR-generative adversarial network demonstrated enhanced resolution of crack images using high-resolution concrete crack images as the training dataset. Overall, deep learning model-assisted human crack analysis improves crack identification; the combination of our NIR dental imaging system and deep learning models has the potential to assist dentists in crack diagnosis.

Abstract Image

利用多种深度学习方法分析人类牙齿裂纹图像。
牙齿裂缝是最常见的牙科疾病之一,不及时治疗会导致牙齿脱落;牙医也很难找到裂缝的位置,即使使用 X 射线成像也是如此。吲哚菁绿(ICG)辅助近红外荧光(NIRF)牙科成像技术可以解决这一问题,因为近红外光的穿透力很深,而且 ICG 具有优异的荧光特性。本研究从近红外成像视频中提取了 593 幅人类裂纹牙图像和 601 幅非裂纹牙图像。该数据集采用了多种成像分析方法,如分类、物体检测和超分辨率,用于裂纹图像分析。结果表明,机器学习方法可以帮助有效地分析牙齿裂纹:使用预训练的残差网络和 squeezenet1_1 模型可以很好地对有裂纹和无裂纹的牙齿图像进行分类,分类准确率分别为 88.2% 和 94.25%;单枪多箱检测器(SSD)能够识别裂纹,即使输入图像的尺寸与原始裂纹图像不同;超分辨率(SR)模型、SR-生成对抗网络使用高分辨率混凝土裂纹图像作为训练数据集,证明了裂纹图像分辨率的增强。总之,深度学习模型辅助人类裂纹分析提高了裂纹识别能力;我们的近红外牙科成像系统与深度学习模型的结合有望帮助牙医进行裂纹诊断。
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来源期刊
Annals of Biomedical Engineering
Annals of Biomedical Engineering 工程技术-工程:生物医学
CiteScore
7.50
自引率
15.80%
发文量
212
审稿时长
3 months
期刊介绍: Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.
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