Cross-Institutional European Evaluation and Validation of Automated Multilabel Segmentation for Acute Intracerebral Hemorrhage and Complications

Purpose: To evaluate a nnU-Net-based deep learning for automated segmentation of intracerebral hemorrhage (ICH), intraventricular hemorrhage (IVH), and perihematomal edema (PHE) on noncontrast CT scans. Materials and Methods: Retrospective data from acute ICH patients admitted at four European stroke centers (2017-2019), along healthy controls (2022-2023), were analyzed. nnU-Net was trained (n=775) using a 5-fold cross-valiadtion approach, tested (n=189), and seperatly validated on internal (n=121), external (n=169), and diverse ICH etiologies (n=175) datasets. Interrater-validated ground truth served as the reference standard. Lesion detection, segmentation, and volumetric accuracy were measured, alongside time efficiency versus manual segmentation. Results: Test set results revealed high nnU-Net accuracy (median Dice Similartiy Coefficient (DSC): ICH 0.91, IVH 0.76, PHE 0.71) and volumetric correlation (ICH, IVH: r=0.99; PHE: r=0.92). Sensitivities were high (ICH, PHE: 99%; IVH: 97%), with IVH detection specificities and sensitivities >90% for volumes up to 0.2 ml. Anatomical-specific metrics showed higher performance for lobar and deep hemorrhages (median DSC 0.90 and 0.92, respectively) and lower for brainstem (median DSC 0.70). Concurrent hemorrhages did not affect accuracy, p> 0.05. Across validation sets, segmentation precision was consistent, especially for ICH (median DSC 0.85-0.90), with PHE slightly lower (median DSC 0.61-0.66) and IVH best in the second and third set (median DSC 0.80). Average processing time was 18.2 seconds versus 18.01 minutes manually. Conclusion: The nnU-Net provides reliable, time-efficient ICH, IVH, and PHE segmentation, validated across various clinical settings, with excellent anatomical-specific performance for lobar and deep hemorrhages. It shows promise for enhancing clinical workflow and research initiatives.