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, segmentation, and volumetric accuracy were evaluated using the Dice similarity coefficient (DSC) and Pearson correlation coefficients (r), with subanalyses for anatomical location and impact of other hemorrhage types (subarachnoid, subdural, or epidural hematoma). The model was validated on internal (n = 121) and external (n = 169) datasets. Processing time was compared to manual segmentation.

Results: Test set sensitivity was 99% for ICH and PHE and 97% for IVH. Segmentation achieved a DSC of 0.91 (ICH), 0.71 (PHE), and 0.76 (IVH), with r = 0.99 (ICH, IVH) and r = 0.92 (PHE). DSC for lobar and deep hemorrhages were 0.90 and 0.92, respectively, and 0.70 in the brainstem, with other hemorrhage types showing no significant impact on segmentation accuracy, P > .05. For internal validation, DSC was 0.88 (ICH), 0.66 (PHE), and 0.80 (IVH), with r of 0.98, 0.88, and 0.98, respectively. External validation yielded DSC values of 0.85 (ICH), 0.61 (PHE), and 0.80 (IVH), with r values of 0.97, 0.85, and 0.96. Mean processing time was 18.2 s (±5 SD), compared to 18.01 min (±20.47 SD) for manual segmentations.

Conclusion: nnU-Net enables reliable, time-efficient segmentation of ICH, PHE, and IVH, validated across multicenter, multivendor datasets of spontaneous ICH, showing potential to enhance clinical workflows.