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

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.

Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.

Purpose: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-based models. The correlation between geometric and dosimetric measures was also investigated to establish whether dosimetric assessment is required for clinical validation.