Evaluation of MR based deep learning contouring for head and neck radiotherapy

This study evaluates the cross acquisition of a deep learning autocontouring model for organ at risk (OAR) delineation in head and neck radiotherapy. Auto-contours for model MRI were trained on diagnostic images and tested on 10 diagnostic, 10 MR radiotherapy planning (RTP), eight MR-Linac (MRL) scans, and, by model CT, on 10 CT planning scans. Goodness of fit scores, dice similarity coefficient (DSC), and distance to agreement (DTA) were calculated for comparison. Read more here: https://www.sciencedirect.com/science/article/abs/pii/S0167814021060813

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