Keywords: Machine Learning/Artificial Intelligence, CancerQuantitative magnetic resonance imaging (qMRI) can provide additional information for diagnosis and response assessment, but adoption of multi-parametric qMRI techniques has been hindered by long acquisition times and labor-intensive processing steps. Magnetic resonance fingerprinting (MRF) provides quantitative maps in a single acquisition but MRF deployment in clinical studies still requires manual delineation of volumes of interest. A spatial-adaptive deep learning framework was developed to segment cervical cancer on MRI and quantify T1 relaxation times of the tumor pre- and post-radiotherapy treatment. Our results suggest that automated segmentation models may be promising tools for quantitative tumor evaluation and treatment response assessment.
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