AIMC Topic: Magnetic Resonance Imaging

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Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks.

IEEE transactions on medical imaging
Fully convolutional networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatia...

Cerebral microbleed detection using Susceptibility Weighted Imaging and deep learning.

NeuroImage
Detecting cerebral microbleeds (CMBs) is important in diagnosing a variety of diseases including dementia, stroke and traumatic brain injury. However, manual detection of CMBs can be time-consuming and prone to errors, whereas the current automatic a...

Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning.

Epilepsy & behavior : E&B
OBJECTIVE: The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy.

Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections.

PloS one
PURPOSE: To compare manual corrections of liver masks produced by a fully automatic segmentation method based on convolutional neural networks (CNN) with manual routine segmentations in MR images in terms of inter-observer variability and interaction...

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detecti...

Identifying predictors of within-person variance in MRI-based brain volume estimates.

NeuroImage
Adequate reliability of measurement is a precondition for investigating individual differences and age-related changes in brain structure. One approach to improve reliability is to identify and control for variables that are predictive of within-pers...

Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network.

NeuroImage
Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue ...

A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods.

Magnetic resonance imaging
In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic Resonance Imaging (MRI) of the human brain. The proposed algorithm not only takes advantages but also solves major drawbacks of two well-known compleme...

Brain tumor detection using statistical and machine learning method.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Brain tumor occurs because of anomalous development of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths can be prevented through early detection of brain tumor. Earlier brain tumo...

Detection of respiratory rate using a classifier of waves in the signal from a FBG-based vital signs sensor.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Monitoring of changes in respiratory rate provides information on a patient's psychophysical state. This paper presents a respiratory rate detection method based on analysis of signals from a fiber Bragg grating (FBG)-based ...