The purpose of this retrospective study was to develop and evaluate the performance of U-Net to determine whether U-Net-based deep learning could accurately perform fully automated localization and segmentation of cervical tumors in MR images, as well as the robustness of extracting apparent diffusion coefficient (ADC) radiomics features. Key points U-Net-based deep learning can perform accurate fully automated localization and segmentation of cervical cancer in diffusion-weighted MR images. Combining b0, b1000, and apparent diffusion coefficient (ADC) images exhibited the highest accuracy in fully automated localization. First-order radiomics feature extraction from whole tumor volume was robust and could thus potentially be used for longitudinal monitoring of treatment responses. Article: Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer Authors: Yu-Chun Lin, Chia-Hung Lin, Hsin-Ying Lu, Hsin-Ju Chiang, Ho-Kai Wang, Yu-Ting Huang, Shu-Hang Ng, Ji-Hong Hong, Tzu-Chen Yen, Chyong-Huey Lai & Gigin Lin
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
