  Many successful methods developed for medical image analysis that are based
on machine learning use supervised learning approaches, which often require
large datasets annotated by experts to achieve high accuracy. However, medical
data annotation is time-consuming and expensive, especially for segmentation
tasks. To solve the problem of learning with limited labeled medical image
data, an alternative deep learning training strategy based on self-supervised
pretraining on unlabeled MRI scans is proposed in this work. Our pretraining
approach first, randomly applies different distortions to random areas of
unlabeled images and then predicts the type of distortions and loss of
information. To this aim, an improved version of Mask-RCNN architecture has
been adapted to localize the distortion location and recover the original image
pixels. The effectiveness of the proposed method for segmentation tasks in
different pre-training and fine-tuning scenarios is evaluated based on the
Osteoarthritis Initiative dataset. Using this self-supervised pretraining
method improved the Dice score by 20% compared to training from scratch. The
proposed self-supervised learning is simple, effective, and suitable for
different ranges of medical image analysis tasks including anomaly detection,
segmentation, and classification.
