Abstract
BackgroundThe ABC/2 method is usually applied to evaluate intracerebral hemorrhage (ICH) volume on computed tomography (CT), although it might be inaccurate and not applicable in estimating extradural or subdural hemorrhage (EDH, SDH) volume due to their irregular hematoma shapes. This study aimed to evaluate deep framework optimized for the segmentation and quantification of ICH, EDH, and SDH.MethodsThe training datasets were 3,000 images retrospectively collected from a collaborating hospital (Hospital A) and segmented by the Dense U-Net framework. Three experienced radiologists determined the ground truth by marking the pixels as hemorrhage area. We utilized the Dice and intra-class correlation coefficients (ICC) to test the reliability of the ground truth. Moreover, the testing datasets consisted of 211 images (internal test) from Hospital A, and 86 ICH images (external test) from another hospital (Hospital B). In this study, we chose scatter plots, ICC, and Pearson correlation coefficients (PCC) with ground truth to evaluate the performance of the deep framework. Furthermore, to validate the effectiveness of the deep framework, we did a comparative analysis of the hemorrhage volume estimation between the deep model and the ABC/2 method.ResultsThe high Dice (0.89–0.95) and ICC (0.985–0.997) showed the consistency of the manual segmentations among the radiologists and the reliability of the ground truth. For the internal test, the Dice coefficients of ICH, EDH, and SDH were 0.90 ± 0.06, 0.88 ± 0.12, and 0.82 ± 0.16, respectively. For the external test, the segmentation Dice was 0.86 ± 0.09. Comparatively, the ICC and PCC of ICH volume estimations were 0.99 performed by Dense U-Net that overmatched the ABC/2 method.ConclusionThis study revealed the excellent performance of hematoma segmentation and volume evaluation based on Dense U-Net, which indicated our deep framework might contribute to efficiently developing treatment strategies for intracranial hemorrhage in clinics.
Highlights
Intracranial hemorrhages, such as intracerebral hemorrhage (ICH), extradural hemorrhage (EDH), subdural hemorrhage (SDH), and subarachnoid hemorrhages (SAH), are dangerous with high mortality and low functional recovery rates (Xi et al, 2006; Qureshi et al, 2009; Soffar, 2012)
The high Dice (0.89–0.95) and intra-class correlation coefficients (ICC) scores (0.985–0.997) showed that the manual segmentations were consistent among the three radiologists and the ground truth was reliable for model training and evaluation
The segmentation-based volume estimation was compared with the ground truth using scatter plots, ICC, and Pearson correlation coefficient (PCC)
Summary
Intracranial hemorrhages, such as ICH, EDH, SDH, and subarachnoid hemorrhages (SAH), are dangerous with high mortality and low functional recovery rates (Xi et al, 2006; Qureshi et al, 2009; Soffar, 2012). The ABC/2 method has been used to calculate the hematoma volume at the time of symptomatic ICH diagnosis in previous studies (Kothari et al, 1996; Divani et al, 2011; Yaghi et al, 2015). With this approach, the hematoma was estimated as an ellipsoid, and A, B, and C were the orthogonal axes measured from CT or magnetic resonance imaging (MRI) (Hemphill and Lam, 2017; Liu et al, 2019). This study aimed to evaluate deep framework optimized for the segmentation and quantification of ICH, EDH, and SDH
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