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

PurposeTo 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 MethodsRetrospective 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. ResultsTest 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. ConclusionThe 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.