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Note: inproceedings

Abstract: Deep neural networks have emerged as the preferred method for semantic segmentation of CT images in recent years. However, understanding their limitations and generalization properties remains an active area of research and a relevant topic for clinical applications. One crucial factor among many is the X-ray radiation dose, which is always kept as low as reasonably possible during CT acquisition. Therefore, potential dose reductions may pose a challenge for existing segmentation models. In this paper, we investigate robustness of the recently proposed TotalSegmentator model for anatomical segmentation with respect to dose reduction. TotalSegmentator combines a large CT dataset and the well-established nnU-Net framework to train deep learning models, resulting in state-of-the-art performance for anatomical segmentation. Our method relies on accurate low-dose simulations derived from acquired full-dose projections. For a set of registered low- and full-dose CT images, we measure the Dice score between the corresponding segmentations. Our results reveal a high level of robustness in the segmentation outcomes. Comprehensive quantitative comparisons demonstrate that at a 20% dose level, the Dice score declines by at most 3%. Visual comparisons reveal only minor differences at the boundaries of the segmented organs. These findings may have a large potential for dose reduction when CT data are acquired predominantly for segmentation purposes, such as for the planning of interventional or surgical procedures.