The detection of new or enlarged white-matter lesions in multiple sclerosis
is a vital task in the monitoring of patients undergoing disease-modifying
treatment for multiple sclerosis. However, the definition of 'new or enlarged'
is not fixed, and it is known that lesion-counting is highly subjective, with
high degree of inter- and intra-rater variability. Automated methods for lesion
quantification hold the potential to make the detection of new and enlarged
lesions consistent and repeatable. However, the majority of lesion segmentation
algorithms are not evaluated for their ability to separate progressive from
stable patients, despite this being a pressing clinical use-case. In this paper
we show that change in volumetric measurements of lesion load alone is not a
good method for performing this separation, even for highly performing
segmentation methods. Instead, we propose a method for identifying lesion
changes of high certainty, and establish on a dataset of longitudinal multiple
sclerosis cases that this method is able to separate progressive from stable
timepoints with a very high level of discrimination (AUC = 0.99), while changes
in 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 method
is able to generalize beyond the setting in which it was trained, achieving an
accuracy of 83% in separating stable and progressive timepoints. Both lesion
volume and count have previously been shown to be strong predictors of disease
course across a population. However, we demonstrate that for individual
patients, changes in these measures are not an adequate means of establishing
no evidence of disease activity. Meanwhile, directly detecting tissue which
changes, with high confidence, from non-lesion to lesion is a feasible
methodology for identifying radiologically active patients.

针对多发性硬化症患者康复过程中的病变检测，本文提出了一种新的自动化分割算法，通过识别病变变化的高确定性来较好地分离稳定型和进展型患者，高于常用的体积变化方法。在个体患者方面，变化的衡量不足以确立无病情进展，直接检测非病变到病变的组织变化是一种切实可行的方法。