Reducing annotation burden in MR segmentation: A novel contrastive learning loss with multi-contrast constraints on local representations

The availability of limited labeled data motivates the use of self-supervised pretraining techniques for deep learning (DL) models. Here, we propose a novel contrastive loss that pushes/pulls local representations within an image based on representational constraints from co-registered multi-contrast MR images that share similar underlying parameters. For multi-organ segmentation tasks in T2-weighted images, pretraining a DL model using the proposed loss function with constraints from co-registered echo images from a radial TSE acquisition, can help reduce annotation burden by 60%. On two independent datasets, proposed pretraining improved Dice scores compared to random initialization and pretraining with conventional contrastive loss.