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

• Published in: European radiology Vol. 30; no. 3; pp. 1297 - 1305
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020; Springer Nature B.V
• Subjects: 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

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 analys...