Context-Aware Voxel-Wise Contrastive Learning for Label Efficient Multi-organ Segmentation

Medical image segmentation is a prerequisite for many clinical applications including disease diagnosis, surgical planning and computer assisted interventions. Due to the challenges in obtaining expert-level accurate, densely annotated multi-organ dataset, existing datasets for multi-organ segmentation either have small number of samples, or only have annotations of a few organs instead of all organs, which are termed as partially labeled data. There exist previous attempts to develop label efficient segmentation method to make use of these partially labeled dataset for improving the performance of multi-organ segmentation. However, most of these methods suffer from the limitation that they only use the labeled information in the dataset without taking advantage of the large amount of unlabeled data. To this end, we propose a context-aware voxel-wise contrastive learning method to take full advantage of both labeled and unlabeled data in partially labeled dataset for an improvement of multi-organ segmentation performance. Experimental Results demonstrated that our proposed method achieved superior performance than other state-of-the-art methods.