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The detection of new or enlarged white-matter lesions in multiple sclerosisis a vital task in the monitoring of patients undergoing disease-modifyingtreatment for multiple sclerosis. However, the definition of 'new or enlarged'is not fixed, and it is known that lesion-counting is highly subjective, withhigh degree of inter- and intra-rater variability. Automated methods for lesionquantification hold the potential to make the detection of new and enlargedlesions consistent and repeatable. However, the majority of lesion segmentationalgorithms are not evaluated for their ability to separate progressive fromstable patients, despite this being a pressing clinical use-case. In this paperwe show that change in volumetric measurements of lesion load alone is not agood method for performing this separation, even for highly performingsegmentation methods. Instead, we propose a method for identifying lesionchanges of high certainty, and establish on a dataset of longitudinal multiplesclerosis cases that this method is able to separate progressive from stabletimepoints with a very high level of discrimination (AUC = 0.99), while changesin lesion volume are much less able to perform this separation (AUC = 0.71).Validation of the method on a second external dataset confirms that the methodis able to generalize beyond the setting in which it was trained, achieving anaccuracy of 83% in separating stable and progressive timepoints. Both lesionvolume and count have previously been shown to be strong predictors of diseasecourse across a population. However, we demonstrate that for individualpatients, changes in these measures are not an adequate means of establishingno evidence of disease activity. Meanwhile, directly detecting tissue whichchanges, with high confidence, from non-lesion to lesion is a feasiblemethodology for identifying radiologically active patients.

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence