Reducing Annotator's Burden: Cross-Pseudo Supervision for Brain Tumor Segmentation

Lidia Luque, Jon André Ottesen, Atle Bjørnerud, Kyrre Eeg Emblem, Bradley J. MacIntosh

Abstract: Deep learning is proven to help with common medical image processing procedures, namely segmentation. Labeling data is a core requirement for training a deep learning model; this is time-consuming and expert annotators are in short supply. Strategies that lower data annotation requirements are highly desirable. In this study, we adapt cross-pseudo supervision (CPS) for 3D medical segmentation, a state-of-art semi-supervised deep-learning method where labeled and unlabeled data are used in conjunction to further improve the resulting model. Using the 2021 BraTS dataset, a fully labeled publicly available brain tumor dataset, we train CPS-based networks using a varying number of labeled and unlabeled samples and compare the resulting models against the fully-supervised baseline. The results show that CPS improves performance scores across all combinations of dataset sizes, with an increase in the Dice similarity coefficient (DSC) of 2.6-4.2% and a decrease in the 95th percentile Hausdorff distance (95% HD) of 24-27%.