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Titel:

Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI.

Dokumenttyp:
Journal Article
Autor(en):
Baur, Christoph; Wiestler, Benedikt; Muehlau, Mark; Zimmer, Claus; Navab, Nassir; Albarqouni, Shadi
Abstract:
Purpose: To develop an unsupervised deep learning model on MR images of normal brain anatomy to automatically detect deviations indicative of pathologic states on abnormal MR images. Materials and Methods: In this retrospective study, spatial autoencoders with skip-connections (which can learn to compress and reconstruct data) were leveraged to learn the normal variability of the brain from MR scans of healthy individuals. A total of 100 normal, in-house MR scans were used for training. Subseque...     »
Zeitschriftentitel:
Radiol Artif Intell
Jahr:
2021
Band / Volume:
3
Heft / Issue:
3
Volltext / DOI:
doi:10.1148/ryai.2021190169
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/34136814
TUM Einrichtung:
Fachgebiet Neuroradiologie (Prof. Zimmer)
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