scholarly journals Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7049
Author(s):  
Chun-Wei Li ◽  
Szu-Yin Lin ◽  
He-Sheng Chou ◽  
Tsung-Yi Chen ◽  
Yu-An Chen ◽  
...  
Keyword(s):  
Image Database ◽  
General Term ◽  
Analysis Model ◽  
Lesion Area ◽  
Pass Filter ◽  
Automatic Diagnosis ◽  
X Ray ◽  
Iterative Thresholding ◽  
Tooth Cleaning ◽  
Apical Lesions

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

scholarly journals Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4613
Author(s):  
Yi-Cheng Mao ◽  
Tsung-Yi Chen ◽  
He-Sheng Chou ◽  
Szu-Yin Lin ◽  
Sheng-Yu Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Rapid Development ◽  
Radiographic Examination ◽  
Analysis Model ◽  
Common Procedure ◽  
Medical Treatments ◽  
Pass Filter ◽  
Single Tooth ◽  
Imaging Research

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

Low pass filter for soft x‐ray monochromators

1990 ◽  
Vol 61 (6) ◽  
pp. 1626-1628 ◽  
Author(s):  
L. J. Terminello ◽  
A. B. McLean ◽  
A. Santoni ◽  
E. Spiller ◽  
F. J. Himpsel
Keyword(s):  
Pass Filter ◽  
Low Pass Filter ◽  
X Ray ◽  
Low Pass

Automatic Diagnosis of Covid-19 Using Chest X-ray Images Through Deep Learning Models

2021 ◽  
pp. 303-312
Author(s):  
Siddharth Gupta ◽  
Palak Aggarwal ◽  
Sumeshwar Singh ◽  
Shiv Ashish Dhondiyal ◽  
Manisha Aeri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Automatic Diagnosis ◽  
X Ray ◽  
Chest X Ray

Soft x-ray low-pass filter with a square-pore microchannel plate

2013 ◽  
Vol 38 (9) ◽  
pp. 1509 ◽  
Author(s):  
Zhurong Cao ◽  
Fengtao Jin ◽  
Jianjun Dong ◽  
Zhenghua Yang ◽  
Xiayu Zhan ◽  
...  
Keyword(s):  
Microchannel Plate ◽  
Pass Filter ◽  
Low Pass Filter ◽  
X Ray ◽  
Low Pass

Crystal-to-Crystal Diastereoselective Transformation of 2,4,6-Triisopropyl-4'-(S)-phenylalaninocarbonylbenzophenone Methyl Ester

1998 ◽  
Vol 54 (6) ◽  
pp. 907-911 ◽  
Author(s):  
H. Hosomi ◽  
Y. Ito ◽  
S. Ohba
Keyword(s):  
Crystal Structure ◽  
High Pressure ◽  
Methyl Ester ◽  
Absolute Configuration ◽  
Crystal Structure Analysis ◽  
X Ray Diffraction ◽  
Pass Filter ◽  
X Ray ◽  
Ultra High Pressure ◽  
Phenylalanine Methyl Ester

Dissymmetry of the photoproduct was induced by using a chiral substituent, (S)-methylphenylalanine, in the title compound {N-4-(2,4,6-triisopropylbenzoyl)benzoyl]-(S)-phenylalanine methyl ester (I)}. On irradiation with light from a 250 W ultra-high-pressure Hg lamp for 7 h through a long-pass filter, the photoreaction in a crystal was 100% complete without the loss of crystallinity. The crystal structures (I), before, and (II) {N-[4-(7-hydroxy-3,5-diisopropyl-8,8-dimethylbicyclo[4.2.0]octa-1,3,5-trien-7-yl)benzoyl]-(S)-phenylalanine methyl ester}, after photocyclization, have been determined by X-ray diffraction. For comparison, a crystal structure analysis has also been carried out for the photoproduct (III) of the 3′-COOMe derivative after recrystallization {methyl 3-(7-hydroxy-3,5-diisopropyl-8,8-dimethylbicyclo[4.2.0]octa-1,3,5-trien-7-yl)benzoate}. The dihedral angle between the central carbonyl plane and the triisopropylphenyl ring deviates from 90° by 10 (1)° in (I), which makes an imbalance in the intramolecular O(carbonyl)...H(methine) distances of the isopropyl groups at positions 2 and 6. The crystal structure of (II) indicates that the nearer methine H was predominantly abstracted by the carbonyl O atom in the reaction. The absolute configuration around the asymmetric C atom in the cyclobutenol ring of the product is S.

Characterization of the surface contamination of deep X-ray lithography mirrors exposed to synchrotron radiation

2018 ◽  
Vol 25 (3) ◽  
pp. 729-737 ◽  
Author(s):  
Sven Achenbach ◽  
Garth Wells ◽  
Chen Shen
Keyword(s):  
Synchrotron Radiation ◽  
White Light ◽  
Exposure Dose ◽  
Correction Factors ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Surface Conditions ◽  
X Ray ◽  
Development Rates ◽  
Mirror Reflectivity

In deep X-ray lithography (DXRL), synchrotron radiation is applied to pattern polymer microstructures. At the Synchrotron Laboratory for Micro and Nano Devices (SyLMAND), Canadian Light Source, a chromium-coated grazing-incidence X-ray double-mirror system is applied as a tunable low-pass filter. In a systematic study, the surface conditions of the two mirrors are analyzed to determine the mirror reflectivity for DXRL process optimization, without the need for spectral analysis or surface probing: PMMA resist foils were homogeneously exposed and developed to determine development rates for mirror angles between 6 mrad and 12 mrad as well as for white light in the absence of the mirrors. Development rates cover almost five orders of magnitude for nominal exposure dose (deposited energy per volume) values of 1 kJ cm−3to 6 kJ cm−3. The rates vary from case to case, indicating that the actual mirror reflectivity deviates from that of clean chromium assumed for the experiments. Fitting the mirror-based development rates to the white-light case as a reference, reflectivity correction factors are identified, and verified by experimental and numerical results of beam calorimetry. The correction factors are related to possible combinations of a varied chromium density, chromium oxidation and a carbon contamination layer. The best fit for all angles is obtained assuming 7.19 g cm−3nominal chromium density, 0.5 nm roughness for all involved layers, and an oxide layer thickness of 25 nm with a carbon top coat of 50 nm to 100 nm. A simulation tool for DXRL exposure parameters was developed to verify that the development rates for all cases do coincide within a small error margin (achieving a reduction of the observed errors by more than two orders of magnitude) if the identified mirror surface conditions are considered when calculating the exposure dose.

scholarly journals Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7116
Author(s):  
Lucas O. Teixeira ◽  
Rodolfo M. Pereira ◽  
Diego Bertolini ◽  
Luiz S. Oliveira ◽  
Loris Nanni ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Image Database ◽  
Dice Coefficient ◽  
Lung Segmentation ◽  
X Ray ◽  
Explainable Artificial Intelligence ◽  
Segmented Images ◽  
Chest X Ray ◽  
The Impact ◽  
Different Sources

COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most. Semantic segmentation was performed using a U-Net CNN architecture, and the classification using three CNN architectures (VGG, ResNet, and Inception). Explainable Artificial Intelligence techniques were employed to estimate the impact of segmentation. A three-classes database was composed: lung opacity (pneumonia), COVID-19, and normal. We assessed the impact of creating a CXR image database from different sources, and the COVID-19 generalization from one source to another. The segmentation achieved a Jaccard distance of 0.034 and a Dice coefficient of 0.982. The classification using segmented images achieved an F1-Score of 0.88 for the multi-class setup, and 0.83 for COVID-19 identification. In the cross-dataset scenario, we obtained an F1-Score of 0.74 and an area under the ROC curve of 0.9 for COVID-19 identification using segmented images. Experiments support the conclusion that even after segmentation, there is a strong bias introduced by underlying factors from different sources.

Analysis model of X-ray coherent scatter images

2010 ◽  
Author(s):  
Taihui Liu ◽  
Jingxin Liu ◽  
Yanhong Li ◽  
Shuang Qiao ◽  
Jianzhong Song
Keyword(s):  
Analysis Model ◽  
Coherent Scatter ◽  
X Ray
Sign in / Sign up

Export Citation Format

Share Document