Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer - 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 (European radiology, 2020)
Keywords: Adult; Aged; Aged, 80 and over; Automation; Cancer; Carcinoma - diagnostic imaging; Carcinoma - pathology; Carcinoma, Squamous Cell - diagnostic imaging; Carcinoma, Squamous Cell - pathology; Cervical cancer; Cervix; Correlation coefficient; Correlation coefficients; Deep Learning; Diagnostic Radiology; Diffusion; Diffusion coefficient; Diffusion Magnetic Resonance Imaging - methods; Feature extraction; Female; Ground truth; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Localization; Machine learning; Magnetic resonance imaging; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Monitoring; Neuroradiology; Order parameters; Patients; Performance evaluation; Radiology; Radiomics; Reproducibility; Reproducibility of Results; Resonance; Retrospective Studies; Robustness; Telemedicine; Training; Tumor Burden; Tumors; Ultrasound; Uterine Cervical Neoplasms - diagnostic imaging; Uterine Cervical Neoplasms - pathology; Young Adult; Diffusion-weighted imaging; Deep learning; Uterine cervical neoplasm; Apparent diffusion coefficient; Radiomics