Publish your Medical Research for FREE | Current Chinese Science

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Aims & Scope | Current Medical Imaging

 

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AIMS & SCOPE

Current Medical Imaging publishes frontier review articles, original research articles, case reports, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques.

The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis. To know more about the Journal, please visit: https://benthamscience.com/journals/current-medical-imaging/

Most Accessed Articles | Assessment of Auditory Pathways Using Diffusion Tensor Imaging in Patients with Neurofibromatosis Type 1

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

 

 

Graphical Abstract:

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Abstract:

Aim: The aim of our study was to determine whether the diffusion properties of the auditory pathways alter between patients with Neurofibromatosis type 1 (NF1) and the healthy subjects. DTI can well demonstrate FA and ADC changes in auditory tracts and it may be a guide to identify the candidates for hearing loss among NF1 children.

Methods: The study population consisted of 43 patients with NF1 and 21 healthy controls. Diffusion tensor imaging (DTI) was used to measure apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values from lemniscus lateralis, colliculus inferior, corpus geniculatum mediale and Heschl’s gyrus. The results were compared with those of the control group.

Results: The ADC values of lateral lemniscus, colliculus inferior and corpus geniculatum mediale were significantly higher in NF1 compared to those of the control group. On the other hand, decreased FA values were observed in lateral lemniscus and colliculus inferior in patients with NF1.

Conclusion: The increase in ADC and reduction in FA in the auditory pathways of patients with NF1 may suggest microstructural alterations, such as a decrease in the number of axons, edema or inflammation in the auditory tracts. To read out more, please visit: http://www.eurekaselect.com/161550/article

Most Cited Articles | Alzheimer’s Disease Classification Based on Multi-feature Fusion

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

 

Author(s): Nuwan Madusanka, Heung-Kook Choi*, Jae-Hong So, Boo-Kyeong Choi.

Graphical Abstract:

post

Abstract:

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD).

Methods: In particular, we classified subjects with Alzheimer‚Äôs disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ¬Ī1.34, 86.61% ¬Ī1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%¬Ī2.15, 84.21%¬Ī1.42 sensitivity and 80.95%¬Ī1.52, 85.00%¬Ī1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ¬Ī 2.18, 78.95% ¬Ī2.26 correct classification rate, 75.00% ¬Ī1.34, 76.19%¬Ī1.84 sensitivity and 77.78% ¬Ī1.14, 82.35% ¬Ī1.34 specificity.

Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance. To read out more, please visit: http://www.eurekaselect.com/166178/article

Open Access Articles | 3D Cascaded Convolutional Networks for Multi-vertebrae Segmentation

 

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

 

Author(s): Liu Xia, Liu Xiao, Gan Quan, Wang Bo*.

 

 

 

Graphical Abstract:

 

image_2020_04_08T07_24_19_625Z

 

Abstract:

Background: Automatic approach to vertebrae segmentation from computed tomography (CT) images is very important in clinical applications. As the intricate appearance and variable architecture of vertebrae across the population, cognate constructions in close vicinity, pathology, and the interconnection between vertebrae and ribs, it is a challenge to propose a 3D automatic vertebrae CT image segmentation method.

Objective: The purpose of this study was to propose an automatic multi-vertebrae segmentation method for spinal CT images.

Methods: Firstly, CLAHE-Threshold-Expansion was preprocessed to improve image quality and reduce input voxel points. Then, 3D coarse segmentation fully convolutional network and cascaded finely segmentation convolutional neural network were used to complete multi-vertebrae segmentation and classification.

Results: The results of this paper were compared with the other methods on the same datasets. Experimental results demonstrated that the Dice similarity coefficient (DSC) in this paper is 94.84%, higher than the V-net and 3D U-net.

Conclusion: Method of this paper has certain advantages in automatically and accurately segmenting vertebrae regions of CT images. Due to the easy acquisition of spine CT images. It was proven to be more conducive to clinical application of treatment that uses our segmentation model to obtain vertebrae regions, combining with the subsequent 3D reconstruction and printing work. To read out more, please visit: http://www.eurekaselect.com/168045/article

Testimonial By Sinan Suzen

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Read what our Authors have to say about publishing in our Journal

Journal Name: Current Drug Metabolism

Contributed Article:  Volatile Inhibitors of Phosphatidylinositol-3-Kinase (PI3K) Pathway: Anticancer Potential of Aroma Compounds of Plant Essential Oils

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Join as Section Editor | Current Dentistry

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Join as SECTION EDITOR for the Journal

“CURRENT DENTISTRY”¬† ¬†

Current Dentistry, a peer-reviewed journal, publishes original research, full-length/mini reviews, and thematic issues in all areas of dental, oral and craniofacial sciences including biological, clinical, materials engineering and bioengineering aspects of dental research. The journal will be of interest to researchers in dental, oral, hard-tissue, and craniofacial science. To know more about the journal, please visit: https://bit.ly/2Vtxn2o

Bentham Science is interested in appointing active Section Editors for the journal; Current Dentistry. If you are working in the related field of the mentioned journal and are interested in becoming Section Editors, please send us your CV and a list of publications. You can also recommend suitable colleagues for the same, and, if possible, send their CV along with their list of publications. 

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Wishing you a very Happy Birthday | Dr. Dimitri P. Mikhailidis

 

Dr. Dimitri

 

Dr. Dimitri P. Mikhailidis serves as the¬†Editor-in-Chief¬†of the Journal¬†‚ÄúCurrent Vascular Pharmacology”

Dimitri P. Mikhailidis
(33295 citations)
Academic Head, Department of Clinical Biochemistry
Royal Free Hospital Campus
University College London Medical School
University College London (UCL)
Pond Street
London, NW3 2QG
UK

Biography

Publish your Medical Research for FREE | Coronaviruses

call for papers

CALL FOR PAPERS! ūüí°

Publish your Medical Research for FREE in our Journal

‚ÄúCoronaviruses‚ÄĚ

‚ėÖ First issue of the journal will be published as Open Access (Free to view) for 3 months only, to ensure a wide readership ‚ėÖ

To submit your paper, email at: hermain@benthamscience.net 

and beena@benthamscience.net

CC: faizan@benthamscience.net

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