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In the recent years, convolutional neural networks have transformed the fieldof medical image analysis due to their capacity to learn discriminative imagefeatures for a variety of classification and regression tasks. However,successfully learning these features requires a large amount of manuallyannotated data, which is expensive to acquire and limited by the availableresources of expert image analysts. Therefore, unsupervised, weakly-supervisedand self-supervised feature learning techniques receive a lot of attention,which aim to utilise the vast amount of available data, while at the same timeavoid or substantially reduce the effort of manual annotation. In this paper,we propose a novel way for training a cardiac MR image segmentation network, inwhich features are learnt in a self-supervised manner by predicting anatomicalpositions. The anatomical positions serve as a supervisory signal and do notrequire extra manual annotation. We demonstrate that this seemingly simple taskprovides a strong signal for feature learning and with self-supervisedlearning, we achieve a high segmentation accuracy that is better than orcomparable to a U-net trained from scratch, especially at a small data setting.When only five annotated subjects are available, the proposed method improvesthe mean Dice metric from 0.811 to 0.852 for short-axis image segmentation,compared to the baseline U-net.

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction