Dose robustness of deep learning models for anatomic segmentation of computed tomography images.

ABSTRACT:

Purpose

The trend towards lower radiation doses and advances in computed tomography (CT) reconstruction may impair the operation of pretrained segmentation models, giving rise to the problem of estimating the dose robustness of existing segmentation models. Previous studies addressing the issue suffer either from a lack of registered low- and full-dose CT images or from simplified simulations.

Approach

We employed raw data from full-dose acquisitions to simulate low-dose CT scans, avoiding the need to rescan a patient. The accuracy of the simulation is validated using a real CT scan of a phantom. We consider down to 20% reduction of radiation dose, for which we measure deviations of several pretrained segmentation models from the full-dose prediction. In addition, compatibil

SUBMITTER: Tsanda A 

PROVIDER: S-EPMC11293838 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature