Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning

被引:0
作者
Ekin Ermiş
Alain Jungo
Robert Poel
Marcela Blatti-Moreno
Raphael Meier
Urspeter Knecht
Daniel M. Aebersold
Michael K. Fix
Peter Manser
Mauricio Reyes
Evelyn Herrmann
机构
[1] Bern University Hospital,Department of Radiation Oncology, Inselspital
[2] and University of Bern,Insel Data Science Center, Inselspital
[3] Bern University Hospital,ARTORG Center for Biomedical Research
[4] University of Bern,Institute for Diagnostic and Interventional Neuroradiology, Inselspital
[5] Bern University Hospital,Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital
[6] and University of Bern,undefined
[7] Bern University Hospital,undefined
[8] and University of Bern,undefined
来源
Radiation Oncology | / 15卷
关键词
Glioblastoma; Automatic segmentation; Deep learning; Target definition; MRI;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 90 条
[11]  
Malandain G(2016)Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: a clinical study Comput Med Imaging Graph 52 8-42
[12]  
Chanalet S(2018)3D fully convolutional networks for subcortical segmentation in MRI: a large-scale study Neuroimage. 170 456-4091
[13]  
Deeley MA(2016)ESTRO-ACROP guideline “target delineation of glioblastomas” Radiother Oncol 118 35-708
[14]  
Chen A(2013)Dose-dense Temozolomide for newly diagnosed Glioblastoma: a randomized phase III clinical trial J Clin Oncol 31 4085-996
[15]  
Datteri R(2014)A randomized trial of Bevacizumab for newly diagnosed Glioblastoma N Engl J Med 370 699-194
[16]  
Visser M(2005)Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma N Engl J Med 352 987-806
[17]  
Müller DMJ(2016)Segmentation of Gliomas in pre-operative and post-operative multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework Brainlesion. 10154 184-59
[18]  
van Duijn RJM(2017)Automatic estimation of extent of resection and residual tumor volume of patients with glioblastoma J Neurosurg 127 798-1972
[19]  
Menze BH(2017)Designing image segmentation studies: statistical power, sample size and reference standard quality Med Image Anal 42 44-11
[20]  
Jakab A(2010)Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group J Clin Oncol 28 1963-323