scholarly journals Deep learning-based estimation of Flory–Huggins parameter of A–B block copolymers from cross-sectional images of phase-separated structures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Katsumi Hagita ◽  
Takeshi Aoyagi ◽  
Yuto Abe ◽  
Shinya Genda ◽  
Takashi Honda
Keyword(s):  
Deep Learning ◽  
Diblock Copolymers ◽  
Three Dimensional ◽  
Image Data ◽  
Real Space ◽  
Cross Sectional ◽  
Regression Problem ◽  
Self Consistent Field ◽  
Experimental Image ◽  
Chain Lengths

AbstractIn this study, deep learning (DL)-based estimation of the Flory–Huggins χ parameter of A-B diblock copolymers from two-dimensional cross-sectional images of three-dimensional (3D) phase-separated structures were investigated. 3D structures with random networks of phase-separated domains were generated from real-space self-consistent field simulations in the 25–40 χN range for chain lengths (N) of 20 and 40. To confirm that the prepared data can be discriminated using DL, image classification was performed using the VGG-16 network. We comprehensively investigated the performances of the learned networks in the regression problem. The generalization ability was evaluated from independent images with the unlearned χN. We found that, except for large χN values, the standard deviation values were approximately 0.1 and 0.5 for A-component fractions of 0.2 and 0.35, respectively. The images for larger χN values were more difficult to distinguish. In addition, the learning performances for the 4-class problem were comparable to those for the 8-class problem, except when the χN values were large. This information is useful for the analysis of real experimental image data, where the variation of samples is limited.

From vesicles to micelles: microphase separation of amphiphilic dendrimer copolymers in a selective solvent

Soft Matter ◽  
2015 ◽  
Vol 11 (45) ◽  
pp. 8801-8811 ◽  
Author(s):  
Bo Lin ◽  
Lan Liu ◽  
Shijie Zhang ◽  
Junzuo Huang ◽  
Fuan He ◽  
...  
Keyword(s):  
Microphase Separation ◽  
Three Dimensional ◽  
Real Space ◽  
Excluded Volume ◽  
Selective Solvent ◽  
Excluded Volume Effects ◽  
Consistent Field ◽  
Self Consistent ◽  
Self Consistent Field ◽  
Self Consistent Field Theory

The microphase separation of amphiphilic dendrimer copolymers in a selective solvent with different excluded volume effects (αS) is investigated using three-dimensional real space self-consistent field theory.

A Deep Learning Method for Limited-View Intravascular Photoacoustic Image Reconstruction

2020 ◽  
Vol 10 (11) ◽  
pp. 2707-2713
Author(s):  
Zheng Sun ◽  
Xiangyang Yan
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Imaging Modality ◽  
Image Data ◽  
Structural Similarity ◽  
Simulated Image ◽  
Cross Sectional ◽  
Data Set ◽  
Reconstruction Methods ◽  
Under Sampling

Intravascular photoacoustic tomography (IVPAT) is a newly developed imaging modality in the interventional diagnosis and treatment of coronary artery diseases. Incomplete acoustic measurement caused by limitedview scanning of the detector in the vascular lumen results in under-sampling artifacts and distortion in the images reconstructed by using the standard reconstruction methods. A method for limited-view IVPAT image reconstruction based on deep learning is presented in this paper. A convolutional neural network (CNN) is constructed and trained with computer-simulated image data set. Then, the trained CNN is used to optimize the cross-sectional images of the vessel which are recovered from the incomplete photoacoustic measurements by using the standard time-reversal (TR) algorithm to obtain the images with the improved quality. Results of numerical demonstration indicate that the method can effectively reduce the image distortion and artifacts caused by the limited-view detection. Furthermore, it is superior to the compressed sensing (CS) method in recovering the unmeasured information of the imaging target with the structural similarity around 10% higher than CS reconstruction.

scholarly journals Automated Analysis of Three-dimensional CBCT Images Taken in Natural Head Position That Combines Facial Profile Processing and Multiple Deep-learning Models

2021 ◽  
Author(s):  
Janghoon Ahn ◽  
Thong Phi Nguyen ◽  
Yoon-Ji Kim ◽  
Taeyong Kim ◽  
Jonghun Yoon
Keyword(s):  
Deep Learning ◽  
Profile Analysis ◽  
Traditional Approach ◽  
Three Dimensional ◽  
Image Data ◽  
Automated Analysis ◽  
Head Position ◽  
X Rays ◽  
Learning Models ◽  
Facial Profile

Abstract Analysing cephalometric X-rays, which is mostly performed by orthodontists or dentists, is an indispensable procedure for diagnosis and treatment planning with orthodontic patients. Artificial intelligence, especially deep-learning techniques for analysing image data, shows great potential for medical and dental image analysis and diagnosis. To explore the feasibility of automating measurement of 13 geometric parameters from three-dimensional cone beam computed tomography (CBCT) images taken in a natural head position, we here describe a smart system that combines a facial profile analysis algorithm with deep-learning models. Using multiple views extracted from the CBCT data as the dataset, our proposed method partitions and detects regions of interest by extracting the facial profile and applying Mask-RCNN, a trained decentralized convolutional neural network (CNN) that positions the key parameters. All the techniques are integrated into a software application with a graphical user interface designed for user convenience. To demonstrate the system’s ability to replace human experts, we validated the performance of the proposed method by comparing it with measurements made by two orthodontists and one advanced general dentist using a commercial dental program. The time savings compared with the traditional approach was remarkable, reducing the processing time from about 30 minutes to about 30 seconds.

3D Reconstruction of the Left Ventricle From Four Echocardiographic Projections

2014 ◽  
Author(s):  
Navaneetha Krishnan Rajan ◽  
Zeying Song ◽  
Kenneth R. Hoffmann ◽  
Marek Belohlavek ◽  
Eileen M. McMahon ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Three Dimensional ◽  
Image Data ◽  
Left Ventricular ◽  
The Body ◽  
Patient Specific ◽  
Immersed Boundary ◽  
Cross Sectional ◽  
Standard Format ◽  
Automated Method

The left ventricle (LV) of a human heart receives oxygenated blood from the lungs and pumps it throughout the body via the aortic valve. Characterizing the LV geometry, its motion, and the ventricular flow is critical in assessing the heart’s health. An automated method has been developed in this work to generate a three-dimensional (3D) model of the LV from multiple-axis echocardiography (echo). Image data from three long-axis sections and a basal section is processed to compute spatial nodes on the LV surface. The generated surfaces are output in a standard format such that it can be imported into the curvilinear-immersed boundary (CURVIB) framework for numerical simulation of the flow inside the LV. The 3D LV model can be used for better understanding of the ventricular motion and the simulation framework provides a powerful tool for studying left ventricular flows on a patient specific basis. Future work would incorporate data from additional cross-sectional images.

scholarly journals Vision Measurement of Tunnel Structures with Robust Modelling and Deep Learning Algorithms

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4945 ◽  
Author(s):  
Xiangyang Xu ◽  
Hao Yang
Keyword(s):  
Deep Learning ◽  
Health Monitoring ◽  
Crack Detection ◽  
Three Dimensional ◽  
Image Data ◽  
Measurement Unit ◽  
Crack Identification ◽  
Monitoring Method ◽  
Vision Measurement ◽  
Robust Modelling

The health monitoring of tunnel structures is vital to the safe operation of railway transportation systems. With the increasing mileage of tunnels, regular inspection and health monitoring are urgently demanded for the tunnel structures, especially for information regarding deformation and damage. However, traditional methods of tunnel inspection are time-consuming, expensive and highly dependent on human subjectivity. In this paper, an automatic tunnel monitoring method is investigated based on image data which is collected through the moving vision measurement unit consisting of camera array. Furthermore, geometric modelling and crack inspection algorithms are proposed where a robust three-dimensional tunnel model is reconstructed utilizing a B-spline method and crack identification is conducted by means of a Mask R-CNN network. The innovation of this investigation is that we combine the robust modelling which could be applied for the deformation analysis and the crack detection where a deep learning method is employed to recognize the tunnel cracks intelligently based on image sensors. In this study, experiments were conducted on a subway tunnel structure several kilometers long, and a robust three-dimensional model is generated and the cracks are identified automatically with the image data. The superiority of this proposal is that the comprehensive information of geometry deformation and crack damage can ensure the reliability and improve the accuracy of health monitoring.

scholarly journals Surface-induced phase transitions in thin films of dendrimer block copolymers

e-Polymers ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 76-84
Author(s):  
Bo Lin ◽  
Chen Zheng ◽  
Qingying Zhu ◽  
Fang Xie
Keyword(s):  
Thin Films ◽  
Phase Transitions ◽  
Three Dimensional ◽  
Real Space ◽  
Surface Wetting ◽  
Surface Strength ◽  
Consistent Field ◽  
Self Consistent Field ◽  
Self Consistent Field Theory ◽  
Hard Substrates

AbstractThe phase morphologies and phase transitions of dendrimer block copolymer thin films confined between two homogeneous, planar hard substrates had been investigated by a three-dimensional real space self-consistent field theory (SCFT). From the perspectives of property and strength of the preferential substrate, when the film system confined within neutral substrates, the thinner film was easier to take the undulated and perpendicular cylinder phases. For the attractive preference of the substrate on block segment A, the polymer films tended to take the surface-wetting structures that was composed by block segment A. On the contrary, for the repulsive preference of the substrate on block segment A, a phase transition of cylinder-lamellae could be observed increasing with the relative surface strength of the preferential substrate.

scholarly journals Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas

Genes ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 382 ◽  
Author(s):  
Sen Liang ◽  
Rongguo Zhang ◽  
Dayang Liang ◽  
Tianci Song ◽  
Tao Ai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Decision Making ◽  
Three Dimensional ◽  
Area Under The Curve ◽  
Image Data ◽  
Clinical Decision ◽  
The Cancer Genome Atlas ◽  
World Health ◽  
Validation Dataset ◽  
Who Grade

Non-invasive prediction of isocitrate dehydrogenase (IDH) genotype plays an important role in tumor glioma diagnosis and prognosis. Recently, research has shown that radiology images can be a potential tool for genotype prediction, and fusion of multi-modality data by deep learning methods can further provide complementary information to enhance prediction accuracy. However, it still does not have an effective deep learning architecture to predict IDH genotype with three-dimensional (3D) multimodal medical images. In this paper, we proposed a novel multimodal 3D DenseNet (M3D-DenseNet) model to predict IDH genotypes with multimodal magnetic resonance imaging (MRI) data. To evaluate its performance, we conducted experiments on the BRATS-2017 and The Cancer Genome Atlas breast invasive carcinoma (TCGA-BRCA) dataset to get image data as input and gene mutation information as the target, respectively. We achieved 84.6% accuracy (area under the curve (AUC) = 85.7%) on the validation dataset. To evaluate its generalizability, we applied transfer learning techniques to predict World Health Organization (WHO) grade status, which also achieved a high accuracy of 91.4% (AUC = 94.8%) on validation dataset. With the properties of automatic feature extraction, and effective and high generalizability, M3D-DenseNet can serve as a useful method for other multimodal radiogenomics problems and has the potential to be applied in clinical decision making.

Research on CT Scan Image of Lung Cancer Based on Deep Learning Method in Artificial Intelligence Field

2020 ◽  
Vol 10 (4) ◽  
pp. 934-939
Author(s):  
Xiaochen Yi ◽  
Zongze Sun ◽  
Baolong Yu ◽  
Munan Yang ◽  
Zhuo Zhang
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Three Dimensional ◽  
Image Data ◽  
Heat Map ◽  
Lung Cancer Diagnosis ◽  
Nodule Detection

Cancer is one of the diseases with high mortality in the 21st century, and lung cancer ranks first in all cancer morbidity and mortality. In recent years, with the rise of big data and artificial intelligence, lung cancer-assisted diagnosis based on deep learning has gradually become A popular research topic. Computer-aided lung cancer diagnosis technology is mainly the process of processing and analyzing the lung image data obtained by medical instrument imaging. The process is summarized into four steps: medical image data preprocessing, lung parenchymal segmentation, lung Nodule detection and segmentation, as well as lesion diagnosis. In order to solve the problem that the two-dimensional image model is not applicable to three-dimensional images, this paper proposes a three-dimensional convolutional neural network model suitable for lung cancer diagnosis. The model consists of two parts. The first part is a three-dimensional deep nodule detection network (FCN) model, which generates a heat map of the lung nodules. We can locate the locations of those malignant nodules through the heat map. According to the heat map generated in the first part, the second part selects those malignant nodules that are likely to be large, and then fuses the features of these selected nodules into one feature vector, showing the whole lung scan. Finally, we use this feature to classify and determine whether we have lung cancer.

scholarly journals Awareness of Cryo Electro-Tomography among Dental Students

2020 ◽  
Vol 11 (SPL3) ◽  
pp. 1050-1053
Author(s):  
Nithyanandham Masilamani ◽  
Dhanraj Ganapathy
Keyword(s):  
College Students ◽  
Electron Tomography ◽  
Three Dimensional ◽  
Image Data ◽  
Dental Students ◽  
Imaging Method ◽  
Cross Sectional ◽  
3D Images ◽  
Cryo Electron Tomography ◽  
Diagnostic Applications

CryoElectronomography (CryoET) is indeed an imaging method used to create high resolution (~1-4 nm) three-dimensional viewpoints of specimen, usually physiological macromolecules as well as cell lines. CryoET is really a highly specialized implementation of scanning electron microscopy cryomicroscopy whereby the specimen are scanned since they are tilted, triggering a series of Image data which can be processed to create a 3d image, analogous to 3D images, similar to a CT scan of the human body. This survey was done for assessing the awareness of Cryo electro tomography amongst dental students. This was a questionnaire oriented cross-sectional type of survey comprising 100 dental college students in Chennai. A self-designed questionnaire comprising ten questions based on the knowledge and awareness aboutCryo-electron tomography amongst dental college students. Questionnaires were circulated through an online website survey planet. The questions explored the awareness of using Cryo-electron tomography as a tool to study various biological applications. After the responses were received from 100 participants, data was collected and analyzed .7% are aware about Cryo Electro-tomography. 3% are aware of the mechanism of action of Cryo Electro-tomography. 5% are aware of the diagnostic applications of Cryo Electro-tomography. 3% are aware of the limitations Cryo Electro-tomography.91% are willing to learn about Cryo Electro-tomography. This study concluded that dental students showed less knowledge and awareness toward Cryo Electro-tomography. There are large gaps in the knowledge and attitudes requiring strong remedial measures.

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