Cross-institutional automated multilabel segmentation for acute intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT.

ABSTRACT:

Background

Precise volume quantification of intracerebral hemorrhage (ICH), intraventricular hemorrhage (IVH), and perihematomal edema (PHE) is a critical parameter for guiding therapy decisions, monitoring therapeutic effects over time, and predicting patient outcomes.

Purpose

To evaluate a nnU-Net-based deep learning model for automated, multilesion segmentation on non-contrast CT.

Materials and methods

Retrospective data from acute spontaneous ICH patients admitted to 4 stroke centers (2015-2022) and controls (2022-2023) were analyzed. Manual segmentations served as ground truth with repeated segmentations as reference standard. nnU-Net was trained (n = 775) using 5-fold cross-validation and tested on a holdout set (n = 189). Lesion detection, segment

SUBMITTER: Nawabi J 

PROVIDER: S-EPMC12429176 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature