Spatial-Adaptive Deep Learning Model and Magnetic Resonance Fingerprinting for Segmentation and Quantitative Evaluation of Cervical Cancer

Quantitative 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.