Background and Purpose— Hematoma volume measurements influence prognosis and treatment decisions in patients with spontaneous intracerebral hemorrhage (ICH). The aims of this study are to derive and validate a fully automated segmentation algorithm for ICH volumetric analysis using deep learning methods. Methods— In-patient computed tomography scans of 300 consecutive adults (age ≥18 years) with spontaneous, supratentorial ICH who were enrolled in the ICHOP (Intracerebral Hemorrhage Outcomes Project; 2009–2018) were separated into training (n=260) and test (n=40) datasets. A fully automated segmentation algorithm was derived using convolutional neural networks, and it was trained on manual segmentations from the training dataset. The algorithm’s performance was assessed against manual and semiautomated segmentation methods in the test dataset. Results— The mean volumetric Dice similarity coefficients for the fully automated segmentation algorithm when tested against manual and semiautomated segmentation methods were 0.894±0.264 and 0.905±0.254, respectively. ICH volumes derived from fully automated versus manual ( R 2 =0.981; P <0.0001), fully automated versus semiautomated ( R 2 =0.978; P <0.0001), and semiautomated versus manual ( R 2 =0.990; P <0001) segmentation methods had strong between-group correlations. The fully automated segmentation algorithm (mean 12.0±2.7 s/scan) was significantly faster than both of the manual (mean 201.5±92.2 s/scan; P <0.001) and semiautomated (mean 288.58±160.3 s/scan; P <0.001) segmentation methods. Conclusions— The fully automated segmentation algorithm quantified hematoma volumes from computed tomography scans of supratentorial ICH patients with similar accuracy and substantially greater efficiency compared with manual and semiautomated segmentation methods. External validation of the fully automated segmentation algorithm is warranted.

Background and Purpose— Perihematomal edema (PHE) is a promising surrogate marker of secondary brain injury in patients with spontaneous intracerebral hemorrhage, but it can be challenging to accurately and rapidly quantify. The aims of this study are to derive and internally validate a fully automated segmentation algorithm for volumetric analysis of PHE. Methods— Inpatient computed tomography scans of 400 consecutive adults with spontaneous, supratentorial intracerebral hemorrhage enrolled in the Intracerebral Hemorrhage Outcomes Project (2009–2018) were separated into training (n=360) and test (n=40) datasets. A fully automated segmentation algorithm was derived from manual segmentations in the training dataset using convolutional neural networks, and its performance was compared with that of manual and semiautomated segmentation methods in the test dataset. Results— The mean volumetric dice similarity coefficients for the fully automated segmentation algorithm were 0.838±0.294 and 0.843±0.293 with manual and semiautomated segmentation methods as reference standards, respectively. PHE volumes derived from the fully automated versus manual (r=0.959; P <0.0001), fully automated versus semiautomated (r=0.960; P <0.0001), and semiautomated versus manual (r=0.961; P <0.0001) segmentation methods had strong between-group correlations. The fully automated segmentation algorithm (mean 18.0±1.8 seconds/scan) quantified PHE volumes at a significantly faster rate than both of the manual (mean 316.4±168.8 seconds/scan; P <0.0001) and semiautomated (mean 480.5±295.3 seconds/scan; P <0.0001) segmentation methods. Conclusions— The fully automated segmentation algorithm accurately quantified PHE volumes from computed tomography scans of supratentorial intracerebral hemorrhage patients with high fidelity and greater efficiency compared with manual and semiautomated segmentation methods. External validation of fully automated segmentation for assessment of PHE is warranted.

Stroke is a leading cause of death and disability worldwide and an increasing number of ischemic stroke patients are undergoing pharmacological and mechanical reperfusion. Both human and experimental models of reperfused ischemic stroke have implicated the complement cascade in secondary tissue injury. Most data point to the lectin and alternative pathways as key to activation, and C3a and C5a binding of their receptors as critical effectors of injury. During periods of thrombolysis use to treat stroke, acute experimental complement cascade blockade has been found to rescue tissue and improves functional outcome. Blockade of the complement cascade during the period of tissue reorganization, repair, and recovery is by contrast not helpful and in fact is likely to be deleterious with emerging data suggesting downstream upregulation of the cascade might even facilitate recovery. Successful clinical translation will require the right clinical setting and pharmacologic strategies that are capable of targeting the key effectors early while not inhibiting delayed repair. Early reports in a variety of disease states suggest that such pharmacologic strategies appear to have a favorable risk profile and offer substantial hope for patients.