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1.
Artículo en Inglés | MEDLINE | ID: mdl-39110557

RESUMEN

X-ray imaging frequently introduces varying degrees of metal artifacts to computed tomography (CT) images when metal implants are present. For the metal artifact reduction (MAR) task, existing end-to-end methods often exhibit limited generalization capabilities. While methods based on multiple iterations often suffer from accumulative error, resulting in lower-quality restoration outcomes. In this work, we innovatively present a generalized diffusion model for Metal Artifact Reduction (DiffMAR). The proposed method utilizes a linear degradation process to simulate the physical phenomenon of metal artifact formation in CT images and directly learn an iterative restoration process from paired CT images in the reverse process. During the reverse process of DiffMAR, a Time-Latent Adjustment (TLA) module is designed to adjust time embedding at the latent level, thereby minimizing the accumulative error during iterative restoration. We also designed a structure information extraction (SIE) module to utilize linear interpolation data in the image domain, guiding the generation of anatomical structures during the iterative restoring. This leads to more accurate and robust shadow-free image generation. Comprehensive analysis, including both synthesized data and clinical evidence, confirms that our proposed method surpasses the current state-of-the-art (SOTA) MAR methods in terms of both image generation quality and generalization.

2.
Sci Rep ; 14(1): 17717, 2024 Jul 31.
Artículo en Inglés | MEDLINE | ID: mdl-39085627

RESUMEN

The evolution and mechanism of ground collapse caused by underground water pipeline leakage have become increasingly significant as more urban areas experience collapses. Based on the principle of similarity, and considering the engineering context of road collapses in Anqing City, Anhui Province, this study designed a 3 m × 2 m × 2 m rupture-collapse model test device. Digital Image Correlation (DIC) technology was employed to investigate the erosion process and collapse mechanisms caused by underground pipeline leakage. The results indicate that groundwater seepage provides the driving force for collapses, combined with the migration space provided by defects, collectively triggering the collapses. When groundwater seepage is minimal, the cohesive forces between soil particles maintain soil stability. As groundwater seepage increases, the soil particle framework is eroded, leading to soil structure destabilization and collapse initiation. The depth of collapse significantly influences stress evolution: stress evolution intensity beneath and above the collapse pit is positively correlated with the distance from the collapse pit bottom, but negatively correlated with the distance from the defect. The research provides insights for the early warning and management of ground collapse.

3.
Artículo en Inglés | MEDLINE | ID: mdl-38937280

RESUMEN

OBJECTIVES: To develop and validate a modified deep learning (DL) model based on nnU-net for classifying and segmenting five-class jaw lesions using cone-beam computed tomography (CBCT). METHODS: A total of 368 CBCT scans (37 168 slices) were used to train a multi-class segmentation model. The data underwent manual annotation by two oral and maxillofacial surgeons (OMSs) to serve as ground truth. Sensitivity, specificity, precision, F1-score, and accuracy were used to evaluate the classification ability of the model and doctors, with or without artificial intelligence assistance. The dice similarity coefficient (DSC), average symmetric surface distance (ASSD) and segmentation time were used to evaluate the segmentation effect of the model. RESULTS: The model achieved the dual task of classifying and segmenting jaw lesions in CBCT. For classification, the sensitivity, specificity, precision, and accuracy of the model were 0.871, 0.974, 0.874 and 0.891, respectively, surpassing oral and maxillofacial radiologists (OMFRs) and OMSs, approaching the specialist. With the model's assistance, the classification performance of OMFRs and OMSs improved, particularly for odontogenic keratocyst (OKC) and ameloblastoma (AM), with F1-score improvements ranging from 6.2% to 12.7%. For segmentation, the DSC was 87.2% and the ASSD was 1.359 mm. The model's average segmentation time was 40 ± 9.9 s, contrasting with 25 ± 7.2 min for OMSs. CONCLUSIONS: The proposed DL model accurately and efficiently classified and segmented five classes of jaw lesions using CBCT. In addition, it could assist doctors in improving classification accuracy and segmentation efficiency, particularly in distinguishing confusing lesions (e.g., AM and OKC).

4.
Opt Lett ; 49(10): 2825-2828, 2024 May 15.
Artículo en Inglés | MEDLINE | ID: mdl-38748171

RESUMEN

Based on the longitudinal manipulation of polarization, a special vector optical beam (VOB) with customized polarization variation in propagation direction can be generated, whose properties and applications remain to be studied. Here, the self-healing propagation behaviors of the longitudinally varying VOB after an opaque object are investigated, and the localized polarization responses on the object distance are revealed. On this basis, characteristic parameters are defined to measure the distance of object, achieving a minimum relative error of 0.63% in a longitudinal range of 300 mm. Besides, the correlations and uncoupling methods of object distance and size are discussed. Our studies open new ways to use the structural properties of VOB and may be instructive for laser measurement.

5.
Int J Neural Syst ; 34(7): 2450033, 2024 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-38623651

RESUMEN

Surgical reconstruction of mandibular defects is a clinical routine manner for the rehabilitation of patients with deformities. The mandible plays a crucial role in maintaining the facial contour and ensuring the speech and mastication functions. The repairing and reconstruction of mandible defects is a significant yet challenging task in oral-maxillofacial surgery. Currently, the mainly available methods are traditional digitalized design methods that suffer from substantial artificial operations, limited applicability and high reconstruction error rates. An automated, precise, and individualized method is imperative for maxillofacial surgeons. In this paper, we propose a Stage-wise Residual Attention Generative Adversarial Network (SRA-GAN) for mandibular defect reconstruction. Specifically, we design a stage-wise residual attention mechanism for generator to enhance the extraction capability of mandibular remote spatial information, making it adaptable to various defects. For the discriminator, we propose a multi-field perceptual network, consisting of two parallel discriminators with different perceptual fields, to reduce the cumulative reconstruction errors. Furthermore, we design a self-encoder perceptual loss function to ensure the correctness of mandibular anatomical structures. The experimental results on a novel custom-built mandibular defect dataset demonstrate that our method has a promising prospect in clinical application, achieving the best Dice Similarity Coefficient (DSC) of 94.238% and 95% Hausdorff Distance (HD95) of 4.787.


Asunto(s)
Mandíbula , Reconstrucción Mandibular , Redes Neurales de la Computación , Humanos , Mandíbula/cirugía , Reconstrucción Mandibular/métodos , Atención/fisiología
6.
Appl Opt ; 63(10): 2683-2688, 2024 Apr 01.
Artículo en Inglés | MEDLINE | ID: mdl-38568552

RESUMEN

Different from the scalar optical field with spatially uniform polarization, the vector optical field exhibits inhomogeneous distribution of polarization on the cross section. Manipulating the variation of polarization in a single optical beam is important to acquire a flexible and controllable focused optical field. Previous studies mainly focused on the vector optical field with its polarization varying along a circular trajectory of the Poincaré sphere. Here, we demonstrate the tight focusing behaviors of the vector optical field with the polarization varying along complex curves of the Poincaré sphere, which is generated by the joint modulation of azimuthal phase and amplitude distributions of orthogonally polarized components. The longitudinal polarization component with a multipolar pattern in rotational symmetry can be achieved with similar distribution of the total focused field. The transverse and longitudinal spin angular momentum distributions in the focal space are discussed. Approximately pure transverse spin angular momentum can be constructed and manipulated in the focal space, which provides the possibility to manipulate the 3D spin flux for the applications of nano and spin photonics.

7.
MedComm (2020) ; 5(3): e487, 2024 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-38469547

RESUMEN

Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning-based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR-TB and classify crucial subtypes, including rifampicin-resistant tuberculosis, multidrug-resistant tuberculosis, and extensively drug-resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR-TB. Finally, DeepTB achieved an area under the curve (AUC) up to 0.930 for thoracic abnormality detection and 0.943 for DR-TB diagnosis. Notably, the system demonstrated instructive value in DR-TB subtype classification with AUCs ranging from 0.880 to 0.928. Meanwhile, class activation maps were generated to express a human-understandable visual concept. Together, showing a prominent performance, DeepTB would be impactful in clinical decision-making for DR-TB.

8.
Clin Oral Investig ; 28(3): 198, 2024 Mar 07.
Artículo en Inglés | MEDLINE | ID: mdl-38448657

RESUMEN

OBJECTIVES: This study aimed to use all permanent teeth as the target and establish an automated dental age estimation method across all developmental stages of permanent teeth, accomplishing all the essential steps of tooth determination, tooth development staging, and dental age assessment. METHODS: A three-step framework for automatically estimating dental age was developed for children aged 3 to 15. First, a YOLOv3 network was employed to complete the tasks of tooth localization and numbering on a digital orthopantomogram. Second, a novel network named SOS-Net was established for accurate tooth development staging based on a modified Demirjian method. Finally, the dental age assessment procedure was carried out through a single-group meta-analysis utilizing the statistical data derived from our reference dataset. RESULTS: The performance tests showed that the one-stage YOLOv3 detection network attained an overall mean average precision 50 of 97.50 for tooth determination. The proposed SOS-Net method achieved an average tooth development staging accuracy of 82.97% for a full dentition. The dental age assessment validation test yielded an MAE of 0.72 years with a full dentition (excluding the third molars) as its input. CONCLUSIONS: The proposed automated framework enhances the dental age estimation process in a fast and standard manner, enabling the reference of any accessible population. CLINICAL RELEVANCE: The tooth development staging network can facilitate the precise identification of permanent teeth with abnormal growth, improving the effectiveness and comprehensiveness of dental diagnoses using pediatric orthopantomograms.


Asunto(s)
Aprendizaje Profundo , Humanos , Niño , Tercer Molar , Odontogénesis , Radiografía Panorámica
9.
Materials (Basel) ; 16(7)2023 Mar 30.
Artículo en Inglés | MEDLINE | ID: mdl-37049054

RESUMEN

Super absorbent resin particles used as profile control and water plugging agent remains a deficiency that the particles swells with high speed when absorbing water, resulting in low strength and limited depth of migration. To address this issue, we proposed a thermosensitive particle gel possessing the upper critical solution temperature (UCST), which was synthesized from hydrophobically modified poly(vinyl alcohol)s (PVA) with glutaraldehyde (GA) as a cross-linker. The structure of the hydrogel was characterized by Fourier transform infrared spectrophotometer (FTIR) and nuclear magnetic resonance (NMR). The thermosensitive-transparency measurement and swelling experiment show that the hydrophobic-modified PVA solutions and corresponding hydrogels exhibited thermosensitive phase transition behaviors with lower critical solution temperature (LCST) and UCST. The results indicated that the temperature-induced phase transition behavior of CHPVA hydrogels leads to their retarding swelling property and great potential as an efficient water plugging agent with excellent temperature and salt resistance.

10.
Polymers (Basel) ; 15(7)2023 Apr 01.
Artículo en Inglés | MEDLINE | ID: mdl-37050379

RESUMEN

Preparation of tough and high-strength hydrogels for water plugging in oil fields with an easy-scalable method is still considered to be a challenge. In this study, dialdehyde cellulose nanofibril (DA-CNF) prepared by sodium periodate oxidation, polyamine, 2-acrylamido-2-methylpropane sulfonic acid (AMPS) with sulfonate groups and Acrylamide (AM) as raw materials, CNF reinforced nanocomposite hydrogels were prepared in one step by in-situ polymerization. The tensile strength, and texture stability of the obtained nanocomposite hydrogel were determined. The results showed that the tensile strength and toughness of the obtained nanocomposite hydrogel increased four times compared with control sample due to physical and chemical double crosslinking synergies. Moreover, the texture intensity of DA-CNFs reinforced hydrogel still maintains high stability and strength performance under high salinity conditions. Therefore, DA-CNF reinforced hydrogel has potential application value in both normal and high-salinity environments in oil recovery.

11.
Polymers (Basel) ; 15(6)2023 Mar 14.
Artículo en Inglés | MEDLINE | ID: mdl-36987230

RESUMEN

In drilling and completion projects, sludge is formed as a byproduct when barite and oil are mixed, and later sticks to the casing. This phenomenon has caused a delay in drilling progress, and increased exploration and development costs. Since nano-emulsions have low interfacial surface tension, wetting, and reversal capabilities, this study used nano-emulsions with a particle size of about 14 nm to prepare a cleaning fluid system. This system enhances stability through the network structure in the fiber-reinforced system, and prepares a set of nano-cleaning fluids with adjustable density for ultra-deep wells. The effective viscosity of the nano-cleaning fluid reaches 11 mPa·s, and the system is stable for up to 8 h. In addition, this research independently developed an indoor evaluation instrument. Based on on-site parameters, the performance of the nano-cleaning fluid was evaluated from multiple angles by heating to 150 °C and pressurizing to 3.0 Mpa to simulate downhole temperature and pressure. The evaluation results show that the viscosity and shear value of the nano-cleaning fluid system is greatly affected by the fiber content, and the cleaning efficiency is greatly affected by the concentration of the nano-emulsion. Curve fitting shows that the average processing efficiency could reach 60-85% within 25 min and the cleaning efficiency has a linear relationship with time. The cleaning efficiency has a linear relationship with time, where R2 = 0.98335. The nano-cleaning fluid enables the deconstruction and carrying of the sludge attached to the well wall, which accomplishes the purpose of downhole cleaning.

12.
Comput Methods Programs Biomed ; 229: 107290, 2023 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-36502546

RESUMEN

BACKGROUND AND OBJECTIVES: There is a noticeable gap in diagnostic evidence strength between the thick and thin scans of Low-Dose CT (LDCT) for pulmonary nodule detection. When the thin scans are needed is unknown, especially when aided with an artificial intelligence nodule detection system. METHODS: A case study is conducted with a set of 1,000 pulmonary nodule screening LDCT scans with both thick (5.0mm), and thin (1.0mm) section scans available. Pulmonary nodule detection is performed by human and artificial intelligence models for nodule detection developed using 3D convolutional neural networks (CNNs). The intra-sample consistency is evaluated with thick and thin scans, for both clinical doctor and NN (neural network) models. Free receiver operating characteristic (FROC) is used to measure the accuracy of humans and NNs. RESULTS: Trained NNs outperform humans with small nodules < 6.0mm, which is a good complement to human ability. For nodules > 6.0mm, human and NNs perform similarly while human takes a fractional advantage. By allowing a few more FPs, a significant sensitivity improvement can be achieved with NNs. CONCLUSIONS: There is a performance gap between the thick and thin scans for pulmonary nodule detection regarding both false negatives and false positives. NNs can help reduce false negatives when the nodules are small and trade off the false negatives for sensitivity. A combination of human and trained NNs is a promising way to achieve a fast and accurate diagnosis.


Asunto(s)
Neoplasias Pulmonares , Nódulo Pulmonar Solitario , Humanos , Inteligencia Artificial , Neoplasias Pulmonares/diagnóstico por imagen , Nódulo Pulmonar Solitario/diagnóstico por imagen , Redes Neurales de la Computación , Tomografía Computarizada por Rayos X , Interpretación de Imagen Radiográfica Asistida por Computador
13.
IEEE Trans Med Imaging ; 42(1): 317-328, 2023 01.
Artículo en Inglés | MEDLINE | ID: mdl-36178994

RESUMEN

Radiographic attributes of lung nodules remedy the shortcomings of lung cancer computer-assisted diagnosis systems, which provides interpretable diagnostic reference for doctors. However, current studies fail to dedicate multi-label classification of lung nodules using convolutional neural networks (CNNs) and are inferior in exploiting statistical dependency between the labels. In addition, data imbalance is an indispensable problem to be reckoned with when employing CNNs to perform lung nodule classification. It introduces greater challenges especially in the multi-label classification. In this paper, we propose a method called MLSL-Net to discriminate lung nodule characteristics and simultaneously address the challenges. Particularly, the proposal employs multi-label softmax loss (MLSL) as the performance index, aiming to reduce the ranking errors between the labels and within the labels during training, thereby optimizing ranking loss and AUC directly. Such criterions can better evaluate the classifier's performance on the multi-label imbalanced dataset. Furthermore, a scale factor is introduced based on the investigation of the max surrogate function. Different from preceding usages, the small factor is used so that to narrow the discrepancy of gradients produced by different labels. More interestingly, this factor also facilitates the exploit of label dependency. Experimental results on the LIDC-IDRI dataset as well as another akin dataset demonstrate that MLSL-Net can effectively perform multi-label classification despite the imbalance issue. Meanwhile, the results confirm the responsibility of the factor for capturing label correlations, accordingly leading to more accurate predictions.


Asunto(s)
Neoplasias Pulmonares , Nódulo Pulmonar Solitario , Humanos , Nódulo Pulmonar Solitario/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Neoplasias Pulmonares/diagnóstico por imagen , Redes Neurales de la Computación , Pulmón
14.
Polymers (Basel) ; 15(23)2023 Dec 02.
Artículo en Inglés | MEDLINE | ID: mdl-38232015

RESUMEN

The continuous growth in global energy and chemical raw material demand has drawn significant attention to the development of heavy oil resources. A primary challenge in heavy oil extraction lies in reducing crude oil viscosity. Alkali-surfactant-polymer (ASP) flooding technology has emerged as an effective method for enhancing heavy oil recovery. However, the chromatographic separation of chemical agents presents a formidable obstacle in heavy oil extraction. To address this challenge, we utilized a free radical polymerization method, employing acrylamide, 2-acrylamido-2-methylpropane sulfonic acid, lauryl acrylate, and benzyl acrylate as raw materials. This approach led to the synthesis of a multifunctional amphiphilic polymer known as PAALB, which we applied to the extraction of heavy oil. The structure of PAALB was meticulously characterized using techniques such as infrared spectroscopy and Nuclear Magnetic Resonance Spectroscopy. To assess the effectiveness of PAALB in reducing heavy oil viscosity and enhancing oil recovery, we conducted a series of tests, including contact angle measurements, interfacial tension assessments, self-emulsification experiments, critical association concentration tests, and sand-packed tube flooding experiments. The research findings indicate that PAALB can reduce oil-water displacement, reduce heavy oil viscosity, and improve swept volume upon injection into the formation. A solution of 5000 mg/L PAALB reduced the contact angle of water droplets on the core surface from 106.55° to 34.95°, shifting the core surface from oil-wet to water-wet, thereby enabling oil-water displacement. Moreover, A solution of 10,000 mg/L PAALB reduced the oil-water interfacial tension to 3.32 × 10-4 mN/m, reaching an ultra-low interfacial tension level, thereby inducing spontaneous emulsification of heavy oil within the formation. Under the condition of an oil-water ratio of 7:3, a solution of 10,000 mg/L PAALB can reduce the viscosity of heavy oil from 14,315 mPa·s to 201 mPa·s via the glass bottle inversion method, with a viscosity reduction rate of 98.60%. In sand-packed tube flooding experiments, under the injection volume of 1.5 PV, PAALB increased the recovery rate by 25.63% compared to traditional hydrolyzed polyacrylamide (HPAM) polymer. The insights derived from this research on amphiphilic polymers hold significant reference value for the development and optimization of chemical flooding strategies aimed at enhancing heavy oil recovery.

15.
Front Biosci (Landmark Ed) ; 27(7): 212, 2022 07 04.
Artículo en Inglés | MEDLINE | ID: mdl-35866406

RESUMEN

BACKGROUND: Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability, especially in resource-limited areas. The combination of mobile CT and deep learning technique has inspired innovations in the routine clinical practice. METHODS: This study recruited participants prospectively in two rural sites of western China. A deep learning system was developed to assist clinicians to identify the nodules and evaluate the malignancy with state-of-the-art performance assessed by recall, free-response receiver operating characteristic curve (FROC), accuracy (ACC), area under the receiver operating characteristic curve (AUC). RESULTS: This study enrolled 12,360 participants scanned by mobile CT vehicle, and detected 9511 (76.95%) patients with pulmonary nodules. Majority of participants were female (8169, 66.09%), and never-smokers (9784, 79.16%). After 1-year follow-up, 86 patients were diagnosed with lung cancer, with 80 (93.03%) of adenocarcinoma, and 73 (84.88%) at stage I. This deep learning system was developed to detect nodules (recall of 0.9507; FROC of 0.6470) and stratify the risk (ACC of 0.8696; macro-AUC of 0.8516) automatically. CONCLUSIONS: A novel model for lung cancer screening, the integration mobile CT with deep learning, was proposed. It enabled specialists to increase the accuracy and consistency of workflow and has potential to assist clinicians in detecting early-stage lung cancer effectively.


Asunto(s)
Aprendizaje Profundo , Neoplasias Pulmonares , Nódulos Pulmonares Múltiples , Detección Precoz del Cáncer/métodos , Femenino , Humanos , Neoplasias Pulmonares/patología , Masculino , Nódulos Pulmonares Múltiples/patología , Estudios Retrospectivos , Tomografía Computarizada por Rayos X/métodos
16.
Front Oncol ; 12: 683792, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35646699

RESUMEN

Objectives: Distinction of malignant pulmonary nodules from the benign ones based on computed tomography (CT) images can be time-consuming but significant in routine clinical management. The advent of artificial intelligence (AI) has provided an opportunity to improve the accuracy of cancer risk prediction. Methods: A total of 8950 detected pulmonary nodules with complete pathological results were retrospectively enrolled. The different radiological manifestations were identified mainly as various nodules densities and morphological features. Then, these nodules were classified into benign and malignant groups, both of which were subdivided into finer specific pathological types. Here, we proposed a deep convolutional neural network for the assessment of lung nodules named DeepLN to identify the radiological features and predict the pathologic subtypes of pulmonary nodules. Results: In terms of density, the area under the receiver operating characteristic curves (AUCs) of DeepLN were 0.9707 (95% confidence interval, CI: 0.9645-0.9765), 0.7789 (95%CI: 0.7569-0.7995), and 0.8950 (95%CI: 0.8822-0.9088) for the pure-ground glass opacity (pGGO), mixed-ground glass opacity (mGGO) and solid nodules. As for the morphological features, the AUCs were 0.8347 (95%CI: 0.8193-0.8499) and 0.9074 (95%CI: 0.8834-0.9314) for spiculation and lung cavity respectively. For the identification of malignant nodules, our DeepLN algorithm achieved an AUC of 0.8503 (95%CI: 0.8319-0.8681) in the test set. Pertaining to predicting the pathological subtypes in the test set, the multi-task AUCs were 0.8841 (95%CI: 0.8567-0.9083) for benign tumors, 0.8265 (95%CI: 0.8004-0.8499) for inflammation, and 0.8022 (95%CI: 0.7616-0.8445) for other benign ones, while AUCs were 0.8675 (95%CI: 0.8525-0.8813) for lung adenocarcinoma (LUAD), 0.8792 (95%CI: 0.8640-0.8950) for squamous cell carcinoma (LUSC), 0.7404 (95%CI: 0.7031-0.7782) for other malignant ones respectively in the malignant group. Conclusions: The DeepLN based on deep learning algorithm represented a competitive performance to predict the imaging characteristics, malignancy and pathologic subtypes on the basis of non-invasive CT images, and thus had great possibility to be utilized in the routine clinical workflow.

17.
Head Face Med ; 18(1): 19, 2022 Jun 27.
Artículo en Inglés | MEDLINE | ID: mdl-35761334

RESUMEN

BACKGROUND: The study aims to evaluate the accuracy of the generative adversarial networks (GAN) for reconstructing bony midfacial defects. METHODS: According to anatomy, the bony midface was divided into five subunit structural regions and artificial defects are manually created on the corresponding CT images. GAN is trained to reconstruct artificial defects to their previous normal shape and tested. The clinical defects are reconstructed by the trained GAN, where the midspan defects were used for qualitative evaluation and the unilateral defects were used for quantitative evaluation. The cosine similarity and the mean error are used to evaluate the accuracy of reconstruction. The Mann-Whitney U test is used to detect whether reconstruction errors were consistent in artificial and unilateral clinical defects. RESULTS: This study included 518 normal CT data, with 415 in training set and 103 in testing set, and 17 real patient data, with 2 midspan defects and 15 unilateral defects. Reconstruction of midspan clinical defects assessed by experts is acceptable. The cosine similarity in the reconstruction of artificial defects and unilateral clinical defects is 0.97 ± 0.01 and 0.96 ± 0.01, P = 0.695. The mean error in the reconstruction of artificial defects and unilateral clinical defects is 0.59 ± 0.31 mm and 0.48 ± 0.08 mm, P = 0.09. CONCLUSION: GAN-based virtual reconstruction technology has reached a high accuracy in testing set, and statistical tests suggest that it can achieve similar results in real patient data. This study has preliminarily solved the problem of bony midfacial defect without reference.

18.
Am J Orthod Dentofacial Orthop ; 161(3): e250-e259, 2022 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-34802868

RESUMEN

INTRODUCTION: Cephalometry plays an important role in the diagnosis and treatment of orthodontics and orthognathic surgery. This study intends to develop an automatic landmark location system to make cephalometry more convenient. METHODS: In this study, 512 lateral cephalograms were collected, and 37 landmarks were included. The coordinates of all landmarks in the 512 films were obtained to establish a labeled dataset: 312 were used as a training set, 100 as a validation set, and 100 as a testing set. An automatic landmark location system based on the convolutional neural network was developed. This system consisted of a global detection module and a locally modified module. The lateral cephalogram was first fed into the global module to obtain an initial estimate of the landmark's position, which was then adjusted with the locally modified module to improve accuracy. Mean radial error (MRE) and success detection rate (SDR) within the range of 1-4 mm were used to evaluate the method. RESULTS: The MRE of our validation set was 1.127 ± 1.028 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 45.95%, 89.19%, 97.30%, 97.30%, and 97.30%. The MRE of our testing set was 1.038 ± 0.893 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 54.05%, 91.89%, 97.30%, 100%, 100%, and 100%. CONCLUSIONS: In this study, we proposed a new automatic landmark location system on the basis of the convolutional neural network. The system could detect 37 landmarks with high accuracy. All landmarks are commonly used in clinical practice and could meet the requirements of different cephalometric analysis methods.


Asunto(s)
Redes Neurales de la Computación , Ortodoncia , Puntos Anatómicos de Referencia/diagnóstico por imagen , Cefalometría/métodos , Humanos , Radiografía , Reproducibilidad de los Resultados
19.
ACS Omega ; 6(43): 28587-28597, 2021 Nov 02.
Artículo en Inglés | MEDLINE | ID: mdl-34746554

RESUMEN

To reduce the cost of synthetic organic corrosion inhibitors in corrosion protection, dye wastewater exhibiting a synergistic effect is used with organic corrosion inhibitors to reduce the amount of high-cost molecules. The corrosion inhibition effects of the cationic dye methylene blue (MB) and the anionic dye methyl orange (MO) are tested. The test methods include electrochemical methods, weight-loss tests, and so on. MB exhibits better performance on the tested steel, with the anticorrosion efficiency reaching as high as 75.40%, which is chosen as an additive for organic corrosion inhibitors. After that, an organic inhibitor decamethylene bis-pyridinium dibromide (DBP) is selected for compounding with MB, and the corrosion inhibition effect under different ratios is tested. Similar effects of the compound inhibitor to the pristine sample are obtained at a ratio of MB/DBP = 6:4. In addition to experiments, theoretical calculations have also confirmed that the addition of dye molecules can inhibit corrosion. This research not only provides a way to reuse dye wastewater but also proposes measures to reduce the cost of organic corrosion inhibitors and, at the same time, provides new ideas for environmental protection and metal protection.

20.
Int J Comput Assist Radiol Surg ; 16(6): 895-904, 2021 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-33846890

RESUMEN

PURPOSE: The robust and automatic segmentation of the pulmonary lobe is vital to surgical planning and regional image analysis of pulmonary related diseases in real-time Computer Aided Diagnosis systems. While a number of studies have examined this issue, the segmentation of unclear borders of the five lobes of the lung remains challenging because of incomplete fissures, the diversity of anatomical pulmonary information, and obstructive lesions caused by pulmonary diseases. This study proposes a model called Regularized Pulmonary Lobe Segmentation Network to accurately predict the lobes as well as the borders. METHODS: First, a 3D fully convolutional network is constructed to extract contextual features from computed tomography images. Second, multi-task learning is employed to learn the segmentations of the lobes and the borders between them to train the neural network to better predict the borders via shared representation. Third, a 3D depth-wise separable de-convolution block is proposed for deep supervision to efficiently train the network. We also propose a hybrid loss function by combining cross-entropy loss with focal loss using adaptive parameters to focus on the tissues and the borders of the lobes. RESULTS: Experiments are conducted on a dataset annotated by experienced clinical radiologists. A 4-fold cross-validation result demonstrates that the proposed approach can achieve a mean dice coefficient of 0.9421 and average symmetric surface distance of 1.3546 mm, which is comparable to state of the art methods. The proposed approach has the capability to accurately segment voxels that are near the lung wall and fissure. CONCLUSION: In this paper, a 3D fully convolutional networks framework is proposed to segment pulmonary lobes in chest CT images accurately. Experimental results show the effectiveness of the proposed approach in segmenting the tissues as well as the borders of the lobes.


Asunto(s)
Procesamiento de Imagen Asistido por Computador/métodos , Imagenología Tridimensional/métodos , Enfermedades Pulmonares/diagnóstico , Pulmón/diagnóstico por imagen , Redes Neurales de la Computación , Tomografía Computarizada por Rayos X/métodos , Humanos
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