Architecture Concrete Mesostructure Analysis Based on CT Image Fracture Process

2011 ◽  
Vol 368-373 ◽  
pp. 2638-2641
Author(s):  
Liang Zhao ◽  
Chang Hua Li ◽  
Fa Ning Dang ◽  
Deng Feng Chen
Keyword(s):  
Spatial Distribution ◽  
Volume Expansion ◽  
Fracture Process ◽  
Ct Images ◽  
Spatial Distributions ◽  
Ct Image ◽  
Destruction Process ◽  
Ct Data ◽  
The Relationship ◽  
Variation Process

Scanning observation on meso evolution of fracture in concrete is carried out by means of computerized tomography (CT) on uniaxial compressive condition. The cracks in the mortar expansion, in particular, the bond of mortar and aggregate which is key regions of concrete damaged, are drawn out through CT image and CT data, and the destruction process of the concrete can be divided into four stakes, compression, enlargement, the expansion of the CT crack,and destruction. According to the character of CT image，MMD is used to analyze the CT images of the concrete specimens in 4 stages of deformation. The components of the CT images are classified and the spatial distributions of crack or cavity, mortar and aggregate are obtained. The variation process of the relationship between distributions of crack or cavity magnitude and stress are obtained from classification maps. The specimens experienced the process of condensed, volume expansion, crack propagation, coalescence and failure. The method can not only reflect the spatial distribution of the materials but also simplify the following analyses that follow.

scholarly journals Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Daryl L. X. Fung ◽  
Qian Liu ◽  
Judah Zammit ◽  
Carson Kai-Sang Leung ◽  
Pingzhao Hu
Keyword(s):  
Deep Learning ◽  
Causal Relationship ◽  
Learning Model ◽  
Ct Images ◽  
Ct Image ◽  
Underlying Diseases ◽  
Relationship Of ◽  
The Relationship ◽  
Deep Learning Model ◽  
Lung Ct

Abstract Background Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testing approach. Automatic analysis of the lung CT images is needed to increase the diagnostic efficiency and release the human participant. Deep learning is successful in automatically solving computer vision problems. Thus, it can be introduced to the automatic and rapid COVID-19 CT diagnosis. Many advanced deep learning-based computer vison techniques were developed to increase the model performance but have not been introduced to medical image analysis. Methods In this study, we propose a self-supervised two-stage deep learning model to segment COVID-19 lesions (ground-glass opacity and consolidation) from chest CT images to support rapid COVID-19 diagnosis. The proposed deep learning model integrates several advanced computer vision techniques such as generative adversarial image inpainting, focal loss, and lookahead optimizer. Two real-life datasets were used to evaluate the model’s performance compared to the previous related works. To explore the clinical and biological mechanism of the predicted lesion segments, we extract some engineered features from the predicted lung lesions. We evaluate their mediation effects on the relationship of age with COVID-19 severity, as well as the relationship of underlying diseases with COVID-19 severity using statistic mediation analysis. Results The best overall F1 score is observed in the proposed self-supervised two-stage segmentation model (0.63) compared to the two related baseline models (0.55, 0.49). We also identified several CT image phenotypes that mediate the potential causal relationship between underlying diseases with COVID-19 severity as well as the potential causal relationship between age with COVID-19 severity. Conclusions This work contributes a promising COVID-19 lung CT image segmentation model and provides predicted lesion segments with potential clinical interpretability. The model could automatically segment the COVID-19 lesions from the raw CT images with higher accuracy than related works. The features of these lesions are associated with COVID-19 severity through mediating the known causal of the COVID-19 severity (age and underlying diseases).

scholarly journals Weakly-supervised lesion analysis with a CNN-based framework for COVID-19

2021 ◽  
Author(s):  
Kaichao Wu ◽  
Beth Jelfs ◽  
Xiangyuan Ma ◽  
Ruitian Ke ◽  
Xuerui Tan ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Lesion Detection ◽  
Ct Image ◽  
Medical College ◽  
Lesion Analysis ◽  
Ct Data ◽  
Weakly Supervised

Abstract Lesions of COVID-19 can be visualized clearly by chest CT images, therefore, providing valuable evidence for clinicians when making a diagnosis. However, due to the variety of COVID-19 lesions and the complexity of the manual delineation procedure, automatic analysis of lesions with unknown and diverse types from a CT image remains a challenging task. In this paper we propose a weakly-supervised framework for this task, requiring only a series of normal and abnormal CT images without the need for annotations of the specific locations and types of lesions. Specifically, this framework employs a deep learning-based diagnosis branch for the classification of the CT image and then leverages a lesion identification branch to capture multiple types of lesions. We verify our framework on publicly available datasets and CT data collected from 13 patients of the First Affiliated Hospital of Shantou University Medical College, China. The results show that the proposed framework can achieve state-of-the-art diagnosis prediction, and the extracted lesion features are capable of distinguishing between lesions showing ground glass opacity and consolidation. Further exploration also demonstrates that this framework has the potential to discover lesion types that have not been reported and can potentially be generalized to lesion detection of other chest-based diseases.

Meso Analysis on Soft Soil Uniaxial Consolidation Experiment with CT

2012 ◽  
Vol 204-208 ◽  
pp. 539-544
Author(s):  
Ling Tao Mao ◽  
Dan Zhao ◽  
Kai Zhou ◽  
Ze Xun Yuan ◽  
Ji Li An
Keyword(s):  
Soil Sample ◽  
Soft Soil ◽  
Ct Images ◽  
Soil Samples ◽  
Ct Image ◽  
Pressure Loading ◽  
Consolidation Process ◽  
Marine Deposits ◽  
Structure Changes ◽  
The Relationship

In this paper, marine sediments soft soil of Beigangchi wharf in Tianjin port was scanned by Computer Tomography(CT) in different load during uniaxial consolidation experiment. The CT images were analyzed to research on the relationship between the microcosmic characteristics of Marine deposits soil and it’s compression in Tianjin areas. The results show that: with the increase of the pressure loading, the average grey value of the CT images increases gradually, which illustrates that soil samples are compacted and the density increases. The variance decreasing of CT image grey value indicates that the soil sample get more evenly. Soft soil of Beigangchi wharf in Tianjin port has high compressibility through the changes of grey compression coefficient. This paper can be referenced for the research of the structure changes of the soil sample consolidation process.

scholarly journals Relationship between solitary pulmonary nodule lung cancer and CT image features based on gradual clustering

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 400-404
Author(s):  
Weipeng Zhang
Keyword(s):  
Lung Cancer ◽  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Ct Images ◽  
Image Features ◽  
Ct Image ◽  
Lung Cancers ◽  
Diagnosis Rate ◽  
Malignant Lesions ◽  
The Relationship

Abstract Background The relationship between the medical characteristics of lung cancers and computer tomography (CT) images are explored so as to improve the early diagnosis rate of lung cancers. Methods This research collected CT images of patients with solitary pulmonary nodule lung cancer, and used gradual clustering methodology to classify them. Preliminary classifications were made, followed by continuous modification and iteration to determine the optimal condensation point, until iteration stability was achieved. Reasonable classification results were obtained. Results the clustering results fell into 3 categories. The first type of patients was mostly female, with ages between 50 and 65 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, with pleural indentation; The second type of patients was mostly male with ages between 50 and 80 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, but with no pleural indentation; The third type of patients was also mostly male with ages between 50 and 80 years. CT images for this group showed no abnormalities. Conclusions the application of gradual clustering methodology can scientifically classify CT image features of patients with lung cancer in the initial lesion stage. These findings provide the basis for early detection and treatment of malignant lesions in patients with lung cancer.

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

scholarly journals Insights Into the Correspondence Between Land Use Pattern and Spatial Distribution of Rail Transit Services

2021 ◽  
Author(s):  
Zhongqi Wang ◽  
Qi Han ◽  
Bauke de Vries ◽  
Li Dai
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Operations Management ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Rail Transit ◽  
Land Use Patterns ◽  
Transit Services ◽  
The Relationship

AbstractThe identification of the relationship between land use and transport lays the foundation for integrated land use and transport planning and management. This work aims to investigate how rail transit is linked to land use. The research on the relationship between land use and rail-based transport is dominated by the impacts of rail projects on land use, without an in-depth understanding of the reverse. However, it is important to note that issues of operation management rather than new constructions deserve greater attention for regions with established rail networks. Given that there is a correspondence between land use patterns and spatial distribution of heavy railway transit (HRT) services at such regions, the study area (i.e., the Netherlands) is partitioned by the Voronoi diagram of HRT stations and the causal relationship between land use and HRT services is examined by structural equation modeling (SEM). The case study of Helmond (a Dutch city) shows the potential of the SEM model for discussing the rail station selection problem in a multiple transit station region (MTSR). Furthermore, in this study, the node place model is adapted with the derivatives of the SEM model (i.e., the latent variable scores for rail service levels and land use characteristics), which are assigned as node and place indexes respectively, to analyze and differentiate the integration of land use and HRT services at the regional level. The answer to whether and how land use affects rail transit services from this study strengthens the scientific basis for rail transit operations management. The SEM model and the modified node place model are complementary to be used as analytical and decision-making tools for rail transit-oriented regional development.

scholarly journals Double parton distributions in the pion from lattice QCD

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Gunnar S. Bali ◽  
Luca Castagnini ◽  
Markus Diehl ◽  
Jonathan R. Gaunt ◽  
Benjamin Gläßle ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Lattice Qcd ◽  
Quark Mass ◽  
Matrix Elements ◽  
Parton Distributions ◽  
Almost All ◽  
The Relationship

Abstract We perform a lattice study of double parton distributions in the pion, using the relationship between their Mellin moments and pion matrix elements of two local currents. A good statistical signal is obtained for almost all relevant Wick contractions. We investigate correlations in the spatial distribution of two partons in the pion, as well as correlations involving the parton polarisation. The patterns we observe depend significantly on the quark mass. We investigate the assumption that double parton distributions approximately factorise into a convolution of single parton distributions.

scholarly journals Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images

2021 ◽  
Vol 17 (4) ◽  
pp. 1-16
Author(s):  
Xiaowe Xu ◽  
Jiawei Zhang ◽  
Jinglan Liu ◽  
Yukun Ding ◽  
Tianchen Wang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Denoising ◽  
Ct Images ◽  
High Dose ◽  
Ct Image ◽  
Paired Data ◽  
High Quality ◽  
Computed Tomography Images ◽  
Significant Difference ◽  
Global Cycle

As one of the most commonly ordered imaging tests, the computed tomography (CT) scan comes with inevitable radiation exposure that increases cancer risk to patients. However, CT image quality is directly related to radiation dose, and thus it is desirable to obtain high-quality CT images with as little dose as possible. CT image denoising tries to obtain high-dose-like high-quality CT images (domain Y ) from low dose low-quality CT images (domain X ), which can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain X (noisy images) and a target domain Y (clean images). Recently, the cycle-consistent adversarial denoising network (CCADN) has achieved state-of-the-art results by enforcing cycle-consistent loss without the need of paired training data, since the paired data is hard to collect due to patients’ interests and cardiac motion. However, out of concerns on patients’ privacy and data security, protocols typically require clinics to perform medical image processing tasks including CT image denoising locally (i.e., edge denoising). Therefore, the network models need to achieve high performance under various computation resource constraints including memory and performance. Our detailed analysis of CCADN raises a number of interesting questions that point to potential ways to further improve its performance using the same or even fewer computation resources. For example, if the noise is large leading to a significant difference between domain X and domain Y , can we bridge X and Y with a intermediate domain Z such that both the denoising process between X and Z and that between Z and Y are easier to learn? As such intermediate domains lead to multiple cycles, how do we best enforce cycle- consistency? Driven by these questions, we propose a multi-cycle-consistent adversarial network (MCCAN) that builds intermediate domains and enforces both local and global cycle-consistency for edge denoising of CT images. The global cycle-consistency couples all generators together to model the whole denoising process, whereas the local cycle-consistency imposes effective supervision on the process between adjacent domains. Experiments show that both local and global cycle-consistency are important for the success of MCCAN, which outperforms CCADN in terms of denoising quality with slightly less computation resource consumption.

scholarly journals Role of Hypoxia-Mediated Autophagy in Tumor Cell Death and Survival

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 533
Author(s):  
Rania F. Zaarour ◽  
Bilal Azakir ◽  
Edries Y. Hajam ◽  
Husam Nawafleh ◽  
Nagwa A. Zeinelabdin ◽  
...  
Keyword(s):  
Cell Death ◽  
Type I ◽  
Dependent Manner ◽  
Destruction Process ◽  
Tumor Cell Death ◽  
Complex Regulation ◽  
Cancer Immunotherapies ◽  
The Relationship ◽  
Shed Light

Programmed cell death or type I apoptosis has been extensively studied and its contribution to the pathogenesis of disease is well established. However, autophagy functions together with apoptosis to determine the overall fate of the cell. The cross talk between this active self-destruction process and apoptosis is quite complex and contradictory as well, but it is unquestionably decisive for cell survival or cell death. Autophagy can promote tumor suppression but also tumor growth by inducing cancer-cell development and proliferation. In this review, we will discuss how autophagy reprograms tumor cells in the context of tumor hypoxic stress. We will illustrate how autophagy acts as both a suppressor and a driver of tumorigenesis through tuning survival in a context dependent manner. We also shed light on the relationship between autophagy and immune response in this complex regulation. A better understanding of the autophagy mechanisms and pathways will undoubtedly ameliorate the design of therapeutics aimed at targeting autophagy for future cancer immunotherapies.

scholarly journals ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 268
Author(s):  
Yeganeh Jalali ◽  
Mansoor Fateh ◽  
Mohsen Rezvani ◽  
Vahid Abolghasemi ◽  
Mohammad Hossein Anisi
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Great Success ◽  
Morphological Operations ◽  
Ct Image ◽  
Feature Maps ◽  
Learning Framework ◽  
Lung Ct

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved.

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