scholarly journals Ultrasonography, X-ray and CT imaging findings of a giant pericardial lipoma: Imaging diagnosis and review of the literature

2013 ◽  
Vol 7 (1) ◽  
pp. 195-198 ◽  
Author(s):  
HAOHUI ZHU ◽  
MEIYUN WANG ◽  
DEGUANG FENG ◽  
YAN FENG ◽  
YING REN ◽  
...  
Keyword(s):  
Ct Imaging ◽  
Imaging Findings ◽  
Review Of The Literature ◽  
Imaging Diagnosis ◽  
X Ray

scholarly journals Adrenal Hemangioma: Findings at Multidetector CT with Short Review of the Literature

2011 ◽  
Vol 2011 ◽  
pp. 1-3 ◽  
Author(s):  
Mohamed Abou El-Ghar ◽  
Huda Refaie ◽  
Ahmed El-Hefnawy ◽  
Tarek El-Diasty
Keyword(s):  
Computed Tomography ◽  
Multidetector Ct ◽  
Short Review ◽  
Ct Imaging ◽  
Imaging Findings ◽  
Review Of The Literature

We present the computed tomography (CT) imaging findings of a 44-year-old male with incidentally discovered right adrenal hemangioma displaying imaging pattern of nonadenomatous pattern, associated with multiple hepatic hemangiomata using 64-slice multidetector scanner with reviewing published CT imaging findings with short review of the literature.

Panner's disease: X-ray, MR imaging findings and review of the literature

1995 ◽  
Vol 19 (6) ◽  
pp. 473-476 ◽  
Author(s):  
Jason M. Stoane ◽  
Maurice R. Poplausky ◽  
Jack O. Haller ◽  
Walter E. Berdon
Keyword(s):  
Mr Imaging ◽  
Imaging Findings ◽  
Review Of The Literature ◽  
X Ray ◽  
Panner’S Disease

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

scholarly journals Imaging diagnosis of classical and new pneumoconiosis: predominant reticular HRCT pattern

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Akira Masanori
Keyword(s):  
Computed Tomography ◽  
Interstitial Lung Disease ◽  
Imaging Features ◽  
Imaging Findings ◽  
Imaging Diagnosis ◽  
High Resolution Computed Tomography ◽  
Pleural Fibrosis ◽  
Pathologic Findings ◽  
Characteristic Imaging ◽  
The Individual

AbstractOur understanding of the manifestations of pneumoconioses is evolving in recent years. Associations between novel exposures and diffuse interstitial lung disease have been newly recognized. In advanced asbestosis, two types of fibrosis are seen, probably related to dose of exposure, existence of pleural fibrosis, and the host factor status of the individual. In pneumoconiosis of predominant reticular type, nodular opacities are often seen in the early phase. The nodular pattern is centrilobular, although some in metal lung show perilymphatic distribution, mimicking sarcoidosis. High-resolution computed tomography enables a more comprehensive correlation between the pathologic findings and clinically relevant imaging findings. The clinician must understand the spectrum of characteristic imaging features related to both known dust exposures and to historically recent new dust exposures.

Hemorrhagic cholecystitis: ultrasound and CT imaging findings—a retrospective case review series

2021 ◽  
Author(s):  
Jessie Z. Ramírez Calderón ◽  
Elena Martínez Chamorro ◽  
Laín Ibáñez Sanz ◽  
José C. Albillos Merino ◽  
Susana Borruel Nacenta
Keyword(s):  
Ct Imaging ◽  
Imaging Findings ◽  
Case Review ◽  
Retrospective Case ◽  
Hemorrhagic Cholecystitis
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