Towards Informative Uncertainty Measures for MRI Segmentation in Clinical Practice: Application to Multiple Sclerosis

We approach the problem of quantifying the degree of reliability of supervised deep learning models used by clinicians for automatic multiple sclerosis lesion segmentation on MRI. In particular, we quantify the correspondence of various uncertainty measures to the errors that a deep learning model makes in overall segmentation or lesion detection. The evaluation is done both on in- and out-of- domain datasets (40 and 99 patients respectively), and provides insights about the measures that can point clinicians to potential errors of an automatic algorithm regardless of the distributional shift.