COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training. (arXiv:2304.14030v3 [cs.CV] UPDATED)

Deep learning models have demonstrated remarkable success in multi-organ
segmentation but typically require large-scale datasets with all organs of
interest annotated. However, medical image datasets are often low in sample
size and only partially labeled, i.e., only a subset of organs are annotated.
Therefore, it is crucial to investigate how to learn a unified model on the
available partially labeled datasets to leverage their synergistic potential.
In this paper, we systematically investigate the partial-label segmentation
problem with theoretical and empirical …

arxiv datasets deep learning image image datasets low medical scale segmentation self-training success training