AIMC Topic: Atrophy
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Alteration of the corpus callosum in patients with Alzheimer's disease: Deep learning-based assessment.
PloS one
Dec 23, 2021
BACKGROUND: Several studies have reported changes in the corpus callosum (CC) in Alzheimer's disease. However, the involved region differed according to the study population and study group. Using deep learning technology, we ensured accurate analysi...
Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.
Cell reports. Medicine
Dec 21, 2021
Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date...
Deep learning-based diagnosis of temporal lobe epilepsy associated with hippocampal sclerosis: An MRI study.
Epilepsy research
Nov 21, 2021
PURPOSE: The currently available indicators-sensitivity and specificity of expert radiological evaluation of MRIs-to identify mesial temporal lobe epilepsy (MTLE) associated with hippocampal sclerosis (HS) are deficient, as they cannot be easily asse...
DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease.
NeuroImage
Aug 24, 2021
Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. Howe...
A pipeline to quantify spinal cord atrophy with deep learning: Application to differentiation of MS and NMOSD patients.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Aug 2, 2021
PURPOSE: Quantitative measurement of various anatomical regions of the brain and spinal cord (SC) in MRI images are used as unique biomarkers to consider progress and effects of demyelinating diseases of the central nervous system. This paper present...
Characterizing the Clinical Features and Atrophy Patterns of -Related Frontotemporal Dementia With Disease Progression Modeling.
Neurology
Jun 22, 2021
BACKGROUND AND OBJECTIVE: Mutations in the gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of mutations have grouped all different mutations together and shown an association with focal at...
A column-based deep learning method for the detection and quantification of atrophy associated with AMD in OCT scans.
Medical image analysis
Jun 12, 2021
The objective quantification of retinal atrophy associated with age-related macular degeneration (AMD) is required for clinical diagnosis, follow-up, treatment efficacy evaluation, and clinical research. Spectral Domain Optical Coherence Tomography (...
Automated assessment of glomerulosclerosis and tubular atrophy using deep learning.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
May 2, 2021
In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accurate assessment of glomerulosclerosis and tubular atrophy is crucial for determining kidney acceptance...
Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis.
Journal of neuroimaging : official journal of the American Society of Neuroimaging
Feb 15, 2021
BACKGROUND AND PURPOSE: Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCC...
Improved Segmentation of the Intracranial and Ventricular Volumes in Populations with Cerebrovascular Lesions and Atrophy Using 3D CNNs.
Neuroinformatics
Feb 1, 2021
Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroimaging. We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural networ...
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