  Self-supervised learning is emerging as an effective substitute for transfer
learning from large datasets. In this work, we use kidney segmentation to
explore this idea. The anatomical asymmetry of kidneys is leveraged to define
an effective proxy task for kidney segmentation via self-supervised learning. A
siamese convolutional neural network (CNN) is used to classify a given pair of
kidney sections from CT volumes as being kidneys of the same or different
sides. This knowledge is then transferred for the segmentation of kidneys using
another deep CNN using one branch of the siamese CNN as the encoder for the
segmentation network. Evaluation results on a publicly available dataset
containing computed tomography (CT) scans of the abdominal region shows that a
boost in performance and fast convergence can be had relative to a network
trained conventionally from scratch. This is notable given that no additional
data/expensive annotations or augmentation were used in training.
