AIMC Topic: Magnetic Resonance Imaging
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An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms.
Computational and mathematical methods in medicine
Dec 7, 2021
In this paper, we have proposed a novel methodology based on statistical features and different machine learning algorithms. The proposed model can be divided into three main stages, namely, preprocessing, feature extraction, and classification. In t...
MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status.
Academic radiology
Dec 4, 2021
RATIONALE AND OBJECTIVES: In patients with breast cancer (BC), lymphovascular invasion (LVI) status is considered an important prognostic factor. We aimed to develop machine learning (ML)-based radiomics models for the prediction of LVI status in pat...
Cytocompatible manganese dioxide-based hydrogel nanoreactors for MRI imaging.
Biomaterials advances
Dec 3, 2021
The application of nanoparticles in magnetic resonance imaging (MRI) has been greatly increasing, due to their advantageous properties such as nanoscale dimension and tuneability. In this context, manganese (Mn)-based nanoparticles have been greatly ...
Deep learning-based artificial intelligence applications in prostate MRI: brief summary.
The British journal of radiology
Dec 3, 2021
Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performa...
A deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure.
Nature communications
Dec 3, 2021
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical...
Deep learning based MRI contrast synthesis using full volume prediction using full volume prediction.
Biomedical physics & engineering express
Dec 3, 2021
In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly bene...
The effect of a post-scan processing denoising system on image quality and morphometric analysis.
Journal of neuroradiology = Journal de neuroradiologie
Dec 2, 2021
PURPOSE: MR image quality and subsequent brain morphometric analysis are inevitably affected by noise. The purpose of this study was to evaluate the effectiveness of an artificial intelligence (AI)-based post-scan processing denoising system, intelli...
Development and Practical Implementation of a Deep Learning-Based Pipeline for Automated Pre- and Postoperative Glioma Segmentation.
AJNR. American journal of neuroradiology
Dec 2, 2021
BACKGROUND AND PURPOSE: Quantitative volumetric segmentation of gliomas has important implications for diagnosis, treatment, and prognosis. We present a deep-learning model that accommodates automated preoperative and postoperative glioma segmentatio...
Automatic upper airway segmentation in static and dynamic MRI via anatomy-guided convolutional neural networks.
Medical physics
Dec 2, 2021
PURPOSE: Upper airway segmentation on MR images is a prerequisite step for quantitatively studying the anatomical structure and function of the upper airway and surrounding tissues. However, the complex variability of intensity and shape of anatomica...
A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding.
Scientific reports
Dec 2, 2021
It is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic ...
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