Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples

Ju, ZJ; Wu, QN; Yang, W; Gu, SS; Guo, W; Wang, JY; Ge, RG; Quan, H; Liu, J; Qu, BL

Qu, BL (corresponding author), Peoples Liberat Army Gen Hosp, Dept Radiat Oncol, Med Ctr 1, 28 Fuxing Rd, Beijing 100853, Peoples R China.

ACTA ONCOLOGICA, 2020; 59 (8): 933

Abstract

Background:Efficient and accurate methods are needed to automatically segmenting organs-at-risk (OAR) to accelerate the radiotherapy workflow and decr......

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