  In this work, we propose a novel straightforward method for medical volume
and sequence segmentation with limited annotations. To avert laborious
annotating, the recent success of self-supervised learning(SSL) motivates the
pre-training on unlabeled data. Despite its success, it is still challenging to
adapt typical SSL methods to volume/sequence segmentation, due to their lack of
mining on local semantic discrimination and rare exploitation on volume and
sequence structures. Based on the continuity between slices/frames and the
common spatial layout of organs across volumes/sequences, we introduced a novel
bootstrap self-supervised representation learning method by leveraging the
predictable possibility of neighboring slices. At the core of our method is a
simple and straightforward dense self-supervision on the predictions of local
representations and a strategy of predicting locals based on global context,
which enables stable and reliable supervision for both global and local
representation mining among volumes. Specifically, we first proposed an
asymmetric network with an attention-guided predictor to enforce
distance-specific prediction and supervision on slices within and across
volumes/sequences. Secondly, we introduced a novel prototype-based
foreground-background calibration module to enhance representation consistency.
The two parts are trained jointly on labeled and unlabeled data. When evaluated
on three benchmark datasets of medical volumes and sequences, our model
outperforms existing methods with a large margin of 4.5\% DSC on ACDC, 1.7\% on
Prostate, and 2.3\% on CAMUS. Intensive evaluations reveals the effectiveness
and superiority of our method.
