Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.

Objective: To develop and evaluate the performance of U-Net for fully automated localization and segmentation of cervical tumors in magnetic resonance (MR) images and the robustness of extracting apparent diffusion coefficient (ADC) radiomics features.Methods: This retrospective study involved analy...

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Publicado en:	European Radiology Vol. 30; no. 3; pp. 1297 - 1306
Autores principales:	Lin, Yu-Chun, Lin, Chia-Hung, Lu, Hsin-Ying, Chiang, Hsin-Ju, Wang, Ho-Kai, Huang, Yu-Ting, Ng, Shu-Hang, Hong, Ji-Hong, Yen, Tzu-Chen, Lai, Chyong-Huey, Lin, Gigin
Formato:	diagnostic images research tables/charts Journal Article
Publicado:	Springer Nature 2020
Acceso en línea:	Ver este registro en EBSCOhost