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Limited One-time Sampling Irregularity Map (LOTS-IM): Automatic Unsupervised Quantitative Assessment of White Matter Hyperintensities in Structural Brain Magnetic Resonance Images

https://doi.org/10.1101/334292
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Automatic Irregular Texture Detection in Brain MRI Without Human Supervision
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Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
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New multispectral MRI data fusion technique for white matter lesion segmentation: method and comparison with thresholding in FLAIR images
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A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging
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P85 CEREBRAL SMALL VESSEL DISEASE AND RISK OF INCIDENT STROKE, DEMENTIA AND DEPRESSION, AND ALL-CAUSE MORTALITY: A SYSTEMATIC REVIEW AND META-ANALYSIS
inferredcites →https://doi.org/10.1016/j.compmedimag.2018.02.002openalex-snapshot
Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology
inferredcites →https://doi.org/10.1016/j.media.2016.10.004openalex-snapshot
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
inferredcites →https://doi.org/10.1016/j.neurobiolaging.2013.05.032openalex-snapshot
Brain white matter damage in aging and cognitive ability in youth and older age
inferredcites →https://doi.org/10.1016/j.neuroimage.2008.10.006openalex-snapshot
Association of regional gray matter volume loss and progression of white matter lesions in multiple sclerosis — A longitudinal voxel-based morphometry study
inferredcites →https://doi.org/10.1016/j.neuroimage.2009.09.005openalex-snapshot
A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions
inferredcites →https://doi.org/10.1016/j.neuroimage.2011.11.032openalex-snapshot
An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis
inferredcites →https://doi.org/10.1016/j.neuroimage.2018.07.005openalex-snapshot
Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images
inferredcites →https://doi.org/10.1016/j.nic.2005.09.008openalex-snapshot
The Alzheimer's Disease Neuroimaging Initiative
inferredcites →https://doi.org/10.1016/j.nicl.2017.12.022openalex-snapshot
White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks
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Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images
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Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration
inferredcites →https://doi.org/10.1109/icacsis.2017.8355053openalex-snapshot
Voxel-based irregularity age map (IAM) for brain's white matter hyperintensities in MRI
inferredcites →https://doi.org/10.1109/tmi.2016.2528821openalex-snapshot
Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation
inferredcites →https://doi.org/10.1111/jgs.12331openalex-snapshot
Brain Structural Change and Gait Decline: A Longitudinal Population‐Based Study
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Time-varying weathering in texture space
inferredcites →https://doi.org/10.1159/000341859openalex-snapshot
Close Correlation between Quantitative and Qualitative Assessments of White Matter Lesions
inferredcites →https://doi.org/10.1161/01.str.0000049766.26453.e9openalex-snapshot
Visual Rating of Age-Related White Matter Changes on Magnetic Resonance Imaging
inferredcites →https://doi.org/10.1161/01.str.0000135226.53499.85openalex-snapshot
Stroke Risk Profile Predicts White Matter Hyperintensity Volume
inferredcites →https://doi.org/10.1161/01.str.27.8.1274openalex-snapshot
Clinical Correlates of White Matter Findings on Cranial Magnetic Resonance Imaging of 3301 Elderly People
inferredcites →https://doi.org/10.1161/strokeaha.110.609768openalex-snapshot
White Matter Hyperintensity Penumbra
inferredcites →https://doi.org/10.1212/01.wnl.0000086375.33512.53openalex-snapshot
Changes in DWI and MRS associated with white matter hyperintensities in elderly subjects
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Evolution of Bacterial Consortia in Spontaneously Started Rye Sourdoughs during Two Months of Daily Propagation
inferredcites →https://doi.org/10.1371/journal.pone.0115551openalex-snapshot
Optimized Brain Extraction for Pathological Brains (optiBET)
inferredcites →https://doi.org/10.1371/journal.pone.0127939openalex-snapshot
Use of Brain MRI Atlases to Determine Boundaries of Age-Related Pathology: The Importance of Statistical Method
inferredcites →https://doi.org/10.2214/ajr.149.2.351openalex-snapshot
MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging
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Measures of the Amount of Ecologic Association Between Species
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-030-00320-3_11openalex-snapshot
Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression Using Irregularity Age Map in Brain MRI
inferred← citeshttps://doi.org/10.1007/978-3-030-32248-9_17openalex-snapshot
Predicting the Evolution of White Matter Hyperintensities in Brain MRI Using Generative Adversarial Networks and Irregularity Map
inferred← citeshttps://doi.org/10.1016/j.biotno.2024.08.001openalex-snapshot
aXonica: A support package for MRI based Neuroimaging
inferred← citeshttps://doi.org/10.3389/fnagi.2019.00150openalex-snapshot
Dilated Saliency U-Net for White Matter Hyperintensities Segmentation Using Irregularity Age Map
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