Medical practice makes significant use of imaging scans such as Ultrasound or MRI as a diagnostic tool. They are used in the visual inspection or quantification of medical parameters computed from the images in post-processing. However, the value of such parameters depends much on the user's variability, device, and algorithmic differences. In this paper, we focus on quantifying the variability due to the human factor, which can be primarily addressed by the structured training of a human operator. We focus on a specific emerging cardiovascular \gls{mri} methodology, the T1 mapping, that has proven useful to identify a range of pathological alterations of the myocardial tissue structure. Training, especially in emerging techniques, is typically not standardized, varying dramatically across medical centers and research teams. Additionally, training assessment is mostly based on qualitative approaches. Our work aims to provide a software tool combining traditional clinical metrics and convolutional neural networks to aid the training process by gathering contours from multiple trainees, quantifying discrepancy from local gold standard or standardized guidelines, classifying trainees output based on critical parameters that affect contours variability.