  Recent self-supervised advances in medical computer vision exploit global and
local anatomical self-similarity for pretraining prior to downstream tasks such
as segmentation. However, current methods assume i.i.d. image acquisition,
which is invalid in clinical study designs where follow-up longitudinal scans
track subject-specific temporal changes. Further, existing self-supervised
methods for medically-relevant image-to-image architectures exploit only
spatial or temporal self-similarity and only do so via a loss applied at a
single image-scale, with naive multi-scale spatiotemporal extensions collapsing
to degenerate solutions. To these ends, this paper makes two contributions: (1)
It presents a local and multi-scale spatiotemporal representation learning
method for image-to-image architectures trained on longitudinal images. It
exploits the spatiotemporal self-similarity of learned multi-scale
intra-subject features for pretraining and develops several feature-wise
regularizations that avoid collapsed identity representations; (2) During
finetuning, it proposes a surprisingly simple self-supervised segmentation
consistency regularization to exploit intra-subject correlation. Benchmarked in
the one-shot segmentation setting, the proposed framework outperforms both
well-tuned randomly-initialized baselines and current self-supervised
techniques designed for both i.i.d. and longitudinal datasets. These
improvements are demonstrated across both longitudinal neurodegenerative adult
MRI and developing infant brain MRI and yield both higher performance and
longitudinal consistency.
