Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks.

Shao-Lun Lu^1,2, Fu-Ren Xiao³, Jason Chia-Hsien Cheng^1,4,2, Wen-Chi Yang^1,4, Yueh-Hung Cheng⁵, Yu-Cheng Chang⁵, Jhih-Yuan Lin⁵, Chih-Hung Liang¹, Jen-Tang Lu⁵, Ya-Fang Chen⁶, Feng-Ming Hsu^1,4

¹Division of Radiation Oncology, Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan.

²Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.

³Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan.

⁴Graduate Institute of Oncology, National Taiwan University College of Medicine, Taipei, Taiwan.

⁵Vysioneer Inc., Cambridge, Massachusetts, USA.

⁶Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Stereotactic radiosurgery (SRS), a validated treatment for brain tumors, requires accurate tumor contouring. This manual segmentation process is time-consuming and prone to substantial inter-practitioner variability. Artificial intelligence (AI) with deep neural networks have increasingly been proposed for use in lesion detection and segmentation but have seldom been validated in a cli…

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