The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning

被引：0
|
作者：
Florian Putz [1 ]
Sogand Beirami [2 ]
Manuel Alexander Schmidt [3 ]
Matthias Stefan May [1 ]
Johanna Grigo [2 ]
Thomas Weissmann [2 ]
Philipp Schubert [3 ]
Daniel Höfler [4 ]
Ahmed Gomaa [2 ]
Ben Tkhayat Hassen [3 ]
Sebastian Lettmaier [5 ]
Benjamin Frey [1 ]
Udo S. Gaipl [2 ]
Luitpold V. Distel [1 ]
Sabine Semrau [2 ]
Christoph Bert [1 ]
Rainer Fietkau [2 ]
Yixing Huang [1 ]
机构：
[1] Friedrich-Alexander-Universität Erlangen-Nürnberg,Department of Radiation Oncology, Universitätsklinikum Erlangen
[2] Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN),Institute of Neuroradiology, University Hospital Erlangen
[3] The Bavarian Cancer Research Center (BZKF),Institute of Radiology, University Hospital Erlangen
[4] Friedrich-Alexander-Universität Erlangen-Nürnberg,undefined
[5] Friedrich-Alexander-Universität Erlangen-Nürnberg,undefined
来源：
Strahlentherapie und Onkologie | 2025年 / 201卷 / 3期
关键词：
Deep learning; Interactive segmentation; Glioma; Tumor auto-contouring; Artificial intelligence;
D O I：
10.1007/s00066-024-02313-8
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：255 / 265
页数：10
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