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Title:

Improving Automated Glioma Segmentation in Routine Clinical Use Through Artificial Intelligence-Based Replacement of Missing Sequences With Synthetic Magnetic Resonance Imaging Scans.

Document type:
Journal Article
Author(s):
Thomas, Marie Franziska; Kofler, Florian; Grundl, Lioba; Finck, Tom; Li, Hongwei; Zimmer, Claus; Menze, Björn; Wiestler, Benedikt
Abstract:
OBJECTIVES: Although automated glioma segmentation holds promise for objective assessment of tumor biology and response, its routine clinical use is impaired by missing sequences, for example, due to motion artifacts. The aim of our study was to develop and validate a generative adversarial network for synthesizing missing sequences to allow for a robust automated segmentation. MATERIALS AND METHODS: Our model was trained on data from The Cancer Imaging Archive (n = 238 WHO II-IV gliomas) to syn...     »
Journal title abbreviation:
Invest Radiol
Year:
2022
Journal volume:
57
Journal issue:
3
Pages contribution:
187-193
Fulltext / DOI:
doi:10.1097/RLI.0000000000000828
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/34652289
Print-ISSN:
0020-9996
TUM Institution:
Professur für AI for Image-Guided Diagnosis and Therapy (Prof. Wiestler); Professur für Neuroradiologie (Prof. Zimmer)
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