  Obtaining manual annotations for large datasets for supervised training of
deep learning (DL) models is challenging. The availability of large unlabeled
datasets compared to labeled ones motivate the use of self-supervised
pretraining to initialize DL models for subsequent segmentation tasks. In this
work, we consider two pre-training approaches for driving a DL model to learn
different representations using: a) regression loss that exploits spatial
dependencies within an image and b) contrastive loss that exploits semantic
similarity between pairs of images. The effect of pretraining techniques is
evaluated in two downstream segmentation applications using Magnetic Resonance
(MR) images: a) liver segmentation in abdominal T2-weighted MR images and b)
prostate segmentation in T2-weighted MR images of the prostate. We observed
that DL models pretrained using self-supervision can be finetuned for
comparable performance with fewer labeled datasets. Additionally, we also
observed that initializing the DL model using contrastive loss based
pretraining performed better than the regression loss.
