AIMC Topic: Magnetic Resonance Imaging

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Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.

European radiology
OBJECTIVES: To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ ...

VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning.

Computers in biology and medicine
INTRODUCTION: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing h...

Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: This study aims to identify robust radiomic features for Magnetic Resonance Imaging (MRI), assess feature selection and machine learning methods for overall survival classification of Glioblastoma multiforme patients, and to robustify mod...

Predicting Alzheimer's disease progression using deep recurrent neural networks.

NeuroImage
Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more t...

Rapid whole-heart CMR with single volume super-resolution.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have long acquisition times. Here, we propose significant speed-ups using ...

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture.

PloS one
Accurate segmentation of brain magnetic resonance imaging (MRI) is an essential step in quantifying the changes in brain structure. Deep learning in recent years has been extensively used for brain image segmentation with highly promising performance...

Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIs.

Computer methods and programs in biomedicine
BACKGROUND: Magnetic resonance images (MRI) is the main diagnostic tool for risk stratification and treatment decision in nasopharyngeal carcinoma (NPC). However, the holistic feature information of multi-parametric MRIs has not been fully exploited ...

Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges.

Journal of infection and public health
Cancer is a fatal illness often caused by genetic disorder aggregation and a variety of pathological changes. Cancerous cells are abnormal areas often growing in any part of human body that are life-threatening. Cancer also known as tumor must be qui...

AI-driven attenuation correction for brain PET/MRI: Clinical evaluation of a dementia cohort and importance of the training group size.

NeuroImage
INTRODUCTION: Robust and reliable attenuation correction (AC) is a prerequisite for accurate quantification of activity concentration. In combined PET/MRI, AC is challenged by the lack of bone signal in the MRI from which the AC maps has to be derive...

A Comparative Analysis of Visual Encoding Models Based on Classification and Segmentation Task-Driven CNNs.

Computational and mathematical methods in medicine
Nowadays, visual encoding models use convolution neural networks (CNNs) with outstanding performance in computer vision to simulate the process of human information processing. However, the prediction performances of encoding models will have differe...