Automated confidence estimation in deep learning auto-segmentation for brain organs at risk on MRI for radiotherapy.

ABSTRACT:

Purpose

We have built a novel AI-driven QA method called AutoConfidence (ACo), to estimate segmentation confidence on a per-voxel basis without gold standard segmentations, enabling robust, efficient review of automated segmentation (AS). We have demonstrated this method in brain OAR AS on MRI, using internal and external (third-party) AS models.

Methods

Thirty-two retrospectives, MRI planned, glioma cases were randomly selected from a local clinical cohort for ACo training. A generator was trained adversarialy to produce internal autosegmentations (IAS) with a discriminator to estimate voxel-wise IAS uncertainty, given the input MRI. Confidence maps for each proposed segmentation were produced for operator use in AS editing and were compared with "difference to gold-standar

SUBMITTER: Alzahrani NM 

PROVIDER: S-EPMC11633808 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature