An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
This work provides an incremental improvement in organ segmentation accuracy for medical image analysis, benefiting diagnosis and computer-assisted interventions.
The paper addresses the problem of false positive and false negative regions in organ segmentation from CT volumes, which is common due to high variability in organ shape and tissue similarity. They propose an uncertainty-driven graph convolutional network (GCN) refinement strategy that improves the Dice score by 1% for pancreas and 2% for spleen segmentation compared to the original U-Net prediction, outperforming state-of-the-art CRF refinement.
Organ segmentation in CT volumes is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, convolutional neural networks have dominated the state of the art in this task. However, since this problem presents a challenging environment due to high variability in the organ's shape and similarity between tissues, the generation of false negative and false positive regions in the output segmentation is a common issue. Recent works have shown that the uncertainty analysis of the model can provide us with useful information about potential errors in the segmentation. In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem that is solved by training a graph convolutional network. To test our method we refine the initial output of a 2D U-Net. We validate our framework with the NIH pancreas dataset and the spleen dataset of the medical segmentation decathlon. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen, with respect to the original U-Net's prediction. Finally, we perform a sensitivity analysis on the parameters of our proposal and discuss the applicability to other CNN architectures, the results, and current limitations of the model for future work in this research direction. For reproducibility purposes, we make our code publicly available at https://github.com/rodsom22/gcn_refinement.