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Titel:

Identifying core MRI sequences for reliable automatic brain metastasis segmentation.

Dokumenttyp:
Journal Article
Autor(en):
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...     »
Zeitschriftentitel:
Radiother Oncol
Jahr:
2023
Band / Volume:
188
Volltext / DOI:
doi:10.1016/j.radonc.2023.109901
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/37678623
Print-ISSN:
0167-8140
TUM Einrichtung:
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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