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

SHAO-LUN LUFU-REN XIAOCHIA-HSIEN CHENGWEN-CHI YANGCheng, Yueh-HungYueh-HungChengChang, Yu-ChengYu-ChengChangLin, Jhih-YuanJhih-YuanLinLiang, Chih-HungChih-HungLiangLu, Jen-TangJen-TangLuYA-FANG CHENFENG-MING HSU2021-12-162021-12-16202115228517https://scholars.lib.ntu.edu.tw/handle/123456789/590068Stereotactic 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 clinical setting.enartificial intelligence; brain tumor; deep learning; randomization; stereotactic radiosurgery[SDGs]SDG3Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networksjournal article10.1093/neuonc/noab071337541552-s2.0-85114240336https://api.elsevier.com/content/abstract/scopus_id/85114240336