Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification
This addresses segmentation accuracy and cost issues for medical imaging applications, but it is incremental as it builds on existing self-supervised and transformer-based methods.
The study tackled organ and tumor segmentation by using self-supervised pre-training with transformers, showing significant improvement in segmentation scores and reducing annotation costs compared to fully-supervised methods.
In this study, our goal is to show the impact of self-supervised pre-training of transformers for organ at risk (OAR) and tumor segmentation as compared to costly fully-supervised learning. The proposed algorithm is called Monte Carlo Transformer based U-Net (MC-Swin-U). Unlike many other available models, our approach presents uncertainty quantification with Monte Carlo dropout strategy while generating its voxel-wise prediction. We test and validate the proposed model on both public and one private datasets and evaluate the gross tumor volume (GTV) as well as nearby risky organs' boundaries. We show that self-supervised pre-training approach improves the segmentation scores significantly while providing additional benefits for avoiding large-scale annotation costs.