Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
AVRA: Automatic visual ratings of atrophy from MRI images using recurrent convolutional neural networks
Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Stockholm, Sweden..ORCID iD: 0000-0002-6100-991X
Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Stockholm, Sweden..
Karolinska Inst, Dept Clin Neurosci, Stockholm, Sweden.;Karolinska Univ Hosp, Dept Radiol, Stockholm, Sweden..
Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Stockholm, Sweden..
Show others and affiliations
2019 (English)In: NeuroImage: Clinical, ISSN 0353-8842, E-ISSN 2213-1582, Vol. 23, article id UNSP 101872Article in journal (Refereed) Published
Abstract [en]

Quantifying the degree of atrophy is done clinically by neuroradiologists following established visual rating scales. For these assessments to be reliable the rater requires substantial training and experience, and even then the rating agreement between two radiologists is not perfect. We have developed a model we call AVRA (Automatic Visual Ratings of Atrophy) based on machine learning methods and trained on 2350 visual ratings made by an experienced neuroradiologist. It provides fast and automatic ratings for Scheltens' scale of medial temporal atrophy (MTA), the frontal subscale of Pasquier's Global Cortical Atrophy (GCA-F) scale, and Koedam's scale of Posterior Atrophy (PA). We demonstrate substantial inter-rater agreement between AVRA's and a neuroradiologist ratings with Cohen's weighted kappa values of kappa(w) = 0.74/0.72 (MTA left/right), kappa(w) = 0.62 (GCA-F) and kappa(w) = 0.74 (PA). We conclude that automatic visual ratings of atrophy can potentially have great scientific value, and aim to present AVRA as a freely available toolbox.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD , 2019. Vol. 23, article id UNSP 101872
Keywords [en]
Atrophy, Visual ratings, Machine learning, MRI, Neuroimaging, Radiology
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:kth:diva-261348DOI: 10.1016/j.nicl.2019.101872ISI: 000485804400063PubMedID: 31154242Scopus ID: 2-s2.0-85066258366OAI: oai:DiVA.org:kth-261348DiVA, id: diva2:1357827
Note

QC 20191004

Available from: 2019-10-04 Created: 2019-10-04 Last updated: 2019-10-04Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records BETA

Wang, Chunliang

Search in DiVA

By author/editor
Mårtensson, GustavWang, ChunliangWestman, Eric
By organisation
Medical Imaging
In the same journal
NeuroImage: Clinical
Neurosciences

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 29 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf