Sparse annotation learning for dense volumetric MR image segmentation with uncertainty estimation.

Results demonstrate that our proposed ESA-Net can consistently achieve better segmentation performances even under the extremely sparse annotation setting, highlighting its effectiveness in exploiting information from unlabeled data.