Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials
MW Weiner, DP Veitch, PS Aisen, LA Beckett… - Alzheimer's & …, 2017 - Elsevier
Abstract Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued
development and standardization of methodologies for biomarkers and has provided an …
development and standardization of methodologies for biomarkers and has provided an …
Automated segmentation of tissues using CT and MRI: a systematic review
Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …
body using computed tomography and magnetic resonance imaging has been rapidly …
Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks
Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier
detection of Alzheimer's disease can help with proper treatment and prevent brain tissue …
detection of Alzheimer's disease can help with proper treatment and prevent brain tissue …
3D whole brain segmentation using spatially localized atlas network tiles
Detailed whole brain segmentation is an essential quantitative technique in medical image
analysis, which provides a non-invasive way of measuring brain regions from a clinical …
analysis, which provides a non-invasive way of measuring brain regions from a clinical …
Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI
X Zhuang, J Shen - Medical image analysis, 2016 - Elsevier
A whole heart segmentation (WHS) method is presented for cardiac MRI. This segmentation
method employs multi-modality atlases from MRI and CT and adopts a new label fusion …
method employs multi-modality atlases from MRI and CT and adopts a new label fusion …
State-space model with deep learning for functional dynamics estimation in resting-state fMRI
Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that
different brain regions still actively interact with each other while a subject is at rest, and …
different brain regions still actively interact with each other while a subject is at rest, and …
Knowledge-aided convolutional neural network for small organ segmentation
Accurate and automatic organ segmentation is critical for computer-aided analysis towards
clinical decision support and treatment planning. State-of-the-art approaches have achieved …
clinical decision support and treatment planning. State-of-the-art approaches have achieved …
MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection
Atlas-based automated anatomical labeling is a fundamental tool in medical image
segmentation, as it defines regions of interest for subsequent analysis of structural and …
segmentation, as it defines regions of interest for subsequent analysis of structural and …
A review on brain structures segmentation in magnetic resonance imaging
S González-Villà, A Oliver, S Valverde, L Wang… - Artificial intelligence in …, 2016 - Elsevier
Background and objectives Automatic brain structures segmentation in magnetic resonance
images has been widely investigated in recent years with the goal of helping diagnosis and …
images has been widely investigated in recent years with the goal of helping diagnosis and …
Anatomical attention guided deep networks for ROI segmentation of brain MR images
Brain region-of-interest (ROI) segmentation based on structural magnetic resonance
imaging (MRI) scans is an essential step for many computer-aid medical image analysis …
imaging (MRI) scans is an essential step for many computer-aid medical image analysis …