The impact of uncertainty estimation on radiomic segmentation reproducibility and scan-rescan repeatability in kidney MRI.

ABSTRACT:

Background

Radiomics holds great potential but is hindered by segmentation and scan-rescan variability, which affect the reproducibility and repeatability of radiomic analysis, respectively. Recently, deep learning (DL) has shown promise in improving segmentation accuracy, thereby enhancing radiomic stability. Moreover, including uncertainty quantification into DL models could provide confidence assessments for segmentations, ultimately improving the trustworthiness and robustness of radiomic outputs.

Purpose

This study investigated whether the reproducibility and repeatability of radiomic features, in relation to segmentation and scan-rescan variability, respectively, could be enhanced by extracting features exclusively from confidently segmented regions, rather than from r

SUBMITTER: Damiano R 

PROVIDER: S-EPMC12264328 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature