  Multiple Sclerosis (MS) is a chronic neuroinflammatory disease and
multi-modality MRIs are routinely used to monitor MS lesions. Many automatic MS
lesion segmentation models have been developed and have reached human-level
performance. However, most established methods assume the MRI modalities used
during training are also available during testing, which is not guaranteed in
clinical practice. Previously, a training strategy termed Modality Dropout
(ModDrop) has been applied to MS lesion segmentation to achieve the
state-of-the-art performance with missing modality. In this paper, we present a
novel method dubbed ModDrop++ to train a unified network adaptive to an
arbitrary number of input MRI sequences. ModDrop++ upgrades the main idea of
ModDrop in two key ways. First, we devise a plug-and-play dynamic head and
adopt a filter scaling strategy to improve the expressiveness of the network.
Second, we design a co-training strategy to leverage the intra-subject relation
between full modality and missing modality. Specifically, the intra-subject
co-training strategy aims to guide the dynamic head to generate similar feature
representations between the full- and missing-modality data from the same
subject. We use two public MS datasets to show the superiority of ModDrop++.
Source code and trained models are available at
https://github.com/han-liu/ModDropPlusPlus.
