  Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is
important to quantify disease progression. In recent years, convolutional
neural networks (CNNs) have shown top performance for this task when a large
amount of labeled data is available. However, the accuracy of CNNs suffers when
dealing with few and/or sparsely labeled datasets. A potential solution is to
leverage the information available in large public datasets in conjunction with
a target dataset which only has limited labeled data. In this paper, we propose
a training framework, SSL2 (self-supervised-semi-supervised), for
multi-modality MS lesion segmentation with limited supervision. We adopt
self-supervised learning to leverage the knowledge from large public 3T
datasets to tackle the limitations of a small 7T target dataset. To leverage
the information from unlabeled 7T data, we also evaluate state-of-the-art
semi-supervised methods for other limited annotation settings, such as small
labeled training size and sparse annotations. We use the shifted-window (Swin)
transformer1 as our backbone network. The effectiveness of self-supervised and
semi-supervised training strategies is evaluated in our in-house 7T MRI
dataset. The results indicate that each strategy improves lesion segmentation
for both limited training data size and for sparse labeling scenarios. The
combined overall framework further improves the performance substantially
compared to either of its components alone. Our proposed framework thus
provides a promising solution for future data/label-hungry 7T MS studies.
