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BACKGROUND Cone-beam computed tomography (CBCT) is essential for adaptive radiotherapy (ART) but is limited by poor image quality. Existing deep learning methods for synthetic CT (sCT) generation face a critical trade-off: diffusion models offer high fidelity but are impractically slow, while fast-inference networks of...
The clonogenic assay is the foundational standard for evaluating cellular radiosensitivity, yet its manual quantification is limited by labor-intensive processing, high inter-observer variability, and a reductive binary scoring system. Here, we present and validate a deep learning-based segmentation pipeline for the au...
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