External Validation and Systematic Error Characterization of Automated Stroke Lesion Segmentation with Exploratory Spatial-Clinical Modeling
Abstract
Accurate infarct segmentation is essential for quantitative stroke imaging and reproducible downstream analyses. We performed external validation of a deep learning–based automated stroke lesion segmentation framework on an independent cohort of acute ischemic stroke patients (N=138). Segmentation performance was evaluated using Dice similarity coefficient (DSC), precision, recall, and 95th percentile Hausdorff distance (HD95). The model achieved an overall DSC of 0.78 ± 0.18 and HD95 of 6.9 ± 13.1 mm. Stratified analysis revealed reduced performance in small infarcts (
Fields of Study & Subjects
Medical Image Segmentation Techniques
Acute Ischemic Stroke Management
Brain Tumor Detection and Classification
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 27 February 2026
- Language
- EN
- Document type
- Preprint