Deep learning-based automated segmentation of intracerebral haemorrhage, intraventricular haemorrhage and perihaematomal oedema on non-contrast CT.
Authors
Affiliations (9)
Affiliations (9)
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
- Department of Otorhinolaryngology-Head and Neck Surgery, Leiden University Medical Center, Leiden, The Netherlands.
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
- Department of Neurosurgery, Erasmus University Medical Center, Erasmus MC Stroke Center, Center for Complex Microvascular Surgery, Rotterdam, The Netherlands.
- Department of Neurology, Isala Hospital, Zwolle, The Netherlands.
- Department of Neurology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands.
- Department of Neurology, University Medical Center Groningen, Groningen, The Netherlands.
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands.
Abstract
Precise volumetric evaluation of intracerebral haemorrhage (ICH), intraventricular haemorrhage (IVH) and perihaematomal oedema (PHO) is essential but manual segmentation is time-consuming and susceptible to variability. We aimed to develop and externally validate a deep learning model for simultaneous segmentation of ICH, IVH and PHO on non-contrast CT (NCCT) in patients with spontaneous ICH. A 3D U-net model was trained with 5-fold cross-validation on baseline NCCTs from 301 patients included in 2 prospective multicentre studies. External validation was performed on 141 baseline NCCTs from another multicentre study. Model performance was evaluated against manual ground truth segmentations using the Dice similarity coefficient (DSC), intraclass correlation coefficients (ICC) and Bland-Altman analyses. The model achieved a median DSC of 0.93 (IQR 0.91-0.94) for ICH, 0.75 (IQR 0.57-0.82) for IVH and 0.53 (IQR 0.34-0.65) for PHO. Volume correlations were excellent for ICH (mean absolute and consistency ICC both 0.98 [95% CI 0.98-0.99]) and IVH (absolute ICC 0.97 [95% CI 0.92-0.98]; consistency ICC 0.98 [95% CI 0.96-0.99]), and moderate for PHO (absolute ICC 0.60 [95% CI -0.08-0.85]; consistency ICC 0.82 [95% CI 0.76-0.87]). Bland-Altman analyses demonstrated a bias for ICH of -0.48Â mL (LoA -8.21 to 7.26), for IVH of -1.68Â mL (LoA -7.35 to 3.99) and for PHO of 13.91Â mL (LoA -4.85 to 32.68). The model enables accurate automated segmentation of ICH, while IVH and PHO segmentation remain more challenging. Automated segmentations may already serve as reliable pre-segmentations in research, but require visual assessment and correction, in particular for IVH and PHO.