  Multiple sclerosis is an inflammatory autoimmune demyelinating disease that
is characterized by lesions in the central nervous system. Typically, magnetic
resonance imaging (MRI) is used for tracking disease progression. Automatic
image processing methods can be used to segment lesions and derive quantitative
lesion parameters. So far, methods have focused on lesion segmentation for
individual MRI scans. However, for monitoring disease progression,
\textit{lesion activity} in terms of new and enlarging lesions between two time
points is a crucial biomarker. For this problem, several classic methods have
been proposed, e.g., using difference volumes. Despite their success for
single-volume lesion segmentation, deep learning approaches are still rare for
lesion activity segmentation. In this work, convolutional neural networks
(CNNs) are studied for lesion activity segmentation from two time points. For
this task, CNNs are designed and evaluated that combine the information from
two points in different ways. In particular, two-path architectures with
attention-guided interactions are proposed that enable effective information
exchange between the two time point's processing paths. It is demonstrated that
deep learning-based methods outperform classic approaches and it is shown that
attention-guided interactions significantly improve performance. Furthermore,
the attention modules produce plausible attention maps that have a masking
effect that suppresses old, irrelevant lesions. A lesion-wise false positive
rate of 26.4% is achieved at a true positive rate of 74.2%, which is not
significantly different from the interrater performance.
