Characterizing cartilage microarchitecture on phase-contrast x-ray computed tomography using deep learning with convolutional neural networks

2017 ◽  
Author(s):  
Botao Deng ◽  
Anas Z. Abidin ◽  
Adora M. D'Souza ◽  
Mahesh B. Nagarajan ◽  
Paola Coan ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Phase Contrast ◽  
X Ray ◽  
X Ray Computed

scholarly journals Visualization Ability of Phase-Contrast Synchrotron-Based X-Ray Imaging Using an X-Ray Interferometer in Soft Tissue Tumors

2021 ◽  
Vol 20 ◽  
pp. 153303382110101
Author(s):  
Thet-Thet Lwin ◽  
Akio Yoneyama ◽  
Hiroko Maruyama ◽  
Tohoru Takeda
Keyword(s):  
Computed Tomography ◽  
Soft Tissue ◽  
Spatial Resolution ◽  
Phase Contrast ◽  
Soft Tissue Tumors ◽  
X Ray ◽  
3 Dimensional ◽  
Computed Tomography Images ◽  
X Ray Imaging ◽  
X Ray Computed

Phase-contrast synchrotron-based X-ray imaging using an X-ray interferometer provides high sensitivity and high spatial resolution, and it has the ability to depict the fine morphological structures of biological soft tissues, including tumors. In this study, we quantitatively compared phase-contrast synchrotron-based X-ray computed tomography images and images of histopathological hematoxylin-eosin-stained sections of spontaneously occurring rat testicular tumors that contained different types of cells. The absolute densities measured on the phase-contrast synchrotron-based X-ray computed tomography images correlated well with the densities of the nuclear chromatin in the histological images, thereby demonstrating the ability of phase-contrast synchrotron-based X-ray imaging using an X-ray interferometer to reliably identify the characteristics of cancer cells within solid soft tissue tumors. In addition, 3-dimensional synchrotron-based phase-contrast X-ray computed tomography enables screening for different structures within tumors, such as solid, cystic, and fibrous tissues, and blood clots, from any direction and with a spatial resolution down to 26 μm. Thus, phase-contrast synchrotron-based X-ray imaging using an X-ray interferometer shows potential for being useful in preclinical cancer research by providing the ability to depict the characteristics of tumor cells and by offering 3-dimensional information capabilities.

Image segmentation of nanoscale Zernike phase contrast X-ray computed tomography images

2015 ◽  
Vol 117 (18) ◽  
pp. 183102 ◽  
Author(s):  
Arjun S. Kumar ◽  
Pratiti Mandal ◽  
Yongjie Zhang ◽  
Shawn Litster
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Phase Contrast ◽  
X Ray ◽  
Computed Tomography Images ◽  
X Ray Computed

scholarly journals Testicular seminoma in the aged rat visualized by phase-contrast X-ray computed tomography

2018 ◽  
Vol 7 (10) ◽  
pp. 205846011880665
Author(s):  
Thet-Thet-Lwin ◽  
Akio Yoneyama ◽  
Motoki Imai ◽  
Hiroko Maruyama ◽  
Kazuyuki Hyodo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Phase Contrast ◽  
Morphological Characteristics ◽  
Cellular Structures ◽  
Image Generation ◽  
Testicular Seminoma ◽  
Aged Rat ◽  
X Ray ◽  
Cystic Changes ◽  
X Ray Computed

Spontaneously growing testicular seminoma in the aged rat was imaged by one of the most sensitive imaging modalities, namely, phase-contrast X-ray computed tomography (CT) with crystal X-ray interferometry. Phase-contrast X-ray CT clearly depicted the detailed inner structures of the tumor and provided 20× magnified images compared to light-microscopic images. Phase-contrast X-ray CT images are generated based on density variations in the object, whereas pathological images are based on differentiation of cellular structures, such as the cellular nuclei and cytoplasm. The mechanism of image generation differs between the two techniques: phase-contrast X-ray CT detects even minute differences in the density among pathological structures, depending, for example, on the number and sizes of the nuclei, variations of the cytoplasmic components, and presence/absence of fibrous septa, cystic changes, and hemorrhage. Thus, phase-contrast X-ray CT with a spatial resolution of 26 µm might allow prediction of the morphological characteristics of a tumor even before histopathological processing.

scholarly journals Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

2020 ◽  
Vol 25 (6) ◽  
pp. 553-565 ◽  
Author(s):  
Boran Sekeroglu ◽  
Ilker Ozsahin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Area Under The Curve ◽  
Operating Characteristics ◽  
X Ray ◽  
Life Saving ◽  
Chest X Ray

The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics–area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

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