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Title:

Identifying core MRI sequences for reliable automatic brain metastasis segmentation.

Document type:
Journal Article
Author(s):
Buchner, Josef A; Peeken, Jan C; Etzel, Lucas; Ezhov, Ivan; Mayinger, Michael; Christ, Sebastian M; Brunner, Thomas B; Wittig, Andrea; Menze, Bjoern H; Zimmer, Claus; Meyer, Bernhard; Guckenberger, Matthias; Andratschke, Nicolaus; El Shafie, Rami A; Debus, Jürgen; Rogers, Susanne; Riesterer, Oliver; Schulze, Katrin; Feldmann, Horst J; Blanck, Oliver; Zamboglou, Constantinos; Ferentinos, Konstantinos; Bilger, Angelika; Grosu, Anca L; Wolff, Robert; Kirschke, Jan S; Eitz, Kerstin A; Combs, Stephan...     »
Abstract:
BACKGROUND: Many automatic approaches to brain tumor segmentation employ multiple magnetic resonance imaging (MRI) sequences. The goal of this project was to compare different combinations of input sequences to determine which MRI sequences are needed for effective automated brain metastasis (BM) segmentation. METHODS: We analyzed preoperative imaging (T1-weighted sequence ± contrast-enhancement (T1/T1-CE), T2-weighted sequence (T2), and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence...     »
Journal title abbreviation:
Radiother Oncol
Year:
2023
Journal volume:
188
Fulltext / DOI:
doi:10.1016/j.radonc.2023.109901
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/37678623
Print-ISSN:
0167-8140
TUM Institution:
Institut für KI und Informatik in der Medizin (Prof. Rückert); Klinik und Poliklinik für Neurochirurgie (Prof. Meyer); Klinik und Poliklinik für RadioOnkologie und Strahlentherapie (Prof. Combs); Professur für AI for Image-Guided Diagnosis and Therapy (Prof. Wiestler); Professur für Neuroradiologie (Prof. Zimmer)
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