Graph-Constrained Contrastive Regularization for Semi-Weakly Volumetric Segmentation
"Semantic volume segmentation suffers from the requirement of having voxel-wise annotated ground-truth data, which requires immense effort to obtain. In this work, we investigate how models can be trained from sparsely annotated volumes, i.e. volumes with only individual slices annotated. By formulating the scenario as a semi-weakly supervised problem where only some regions in the volume are annotated, we obtain surprising results: expensive dense volumetric annotations can be replaced by cheap, partially labeled volumes with limited impact on accuracy if the hypothesis space of valid models gets properly constrained during training. With our Contrastive Constrained Regularization (Con2R), we demonstrate that 3D convolutional models can be trained with less than 4% of only two dimensional ground-truth labels and still reach up to 88% accuracy of fully supervised baseline models with dense volumetric annotations. To get insights into Con2Rs success, we study how strong semi-supervised algorithms transfer to our new volumetric semi-weakly supervised setting. In this manner, we explore retinal fluid and brain tumor segmentation and give a detailed look into accuracy progression for scenarios with extremely scarce labels."