Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury - MacIntosh, Bradley J.; Liu, Qinghui; Schellhorn, Till; Beyer, Mona K.; Groote, Inge Rasmus; Morberg, Pål C.; Poulin, Joshua M.; Selseth, Maiken N.; Bakke, Ragnhild C.; Naqvi, Aina; Hillal, Amir; Ullberg, Teresa; et al. (Frontiers in Neurology, 2023)
Introduction: Radiological assessment is necessary to diagnose spontaneous intracerebral haemorrhage (ICH) and traumatic brain injury intracranial haemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the haemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study.Methods: Non-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Haemorrhage location frequency maps were generated. The number of estimated haematoma clusters correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases.Results: The haemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45 -0.74; each p-value < 0.01) and evidence of ICH between-site differences. Relative to hand-drawn annotations for one ICH site, the VIOLA-AI segmentation mask achieved a median Dice Similarity Coefficient of 0.82 (interquartile range: 0.78 and 0.83), a small overestimate in the haematoma volume by a median of 0.47 mL (interquartile range: 0.04 and 1.75 mL). The bleed detection ROC analysis for the whole sample gave a high area-under-the-curve (AUC) of 0.92 (sensitivity and specificity of 83.28% and 95.41%); however, when considering only the mild head injury site, the TBI-bleed detection gave an AUC of 0.70. Discussion: An open-source segmentation tool was used to visualize haemorrhage locations across multiple data sources and revealed quantitative haemorrhage site differences. The automated total haemorrhage volume estimate correlated with the haemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial haemorrhage.