Fully Automated Segmentation Algorithm for Perihematomal Edema Volumetry After Spontaneous Intracerebral Hemorrhage

• Published in: Stroke (1970) 2020-03, Vol.51 (3), p.815-823
• Main Authors: Ironside, Natasha, Chen, Ching-Jen, Mutasa, Simukayi, Sim, Justin L., Ding, Dale, Marfatiah, Saurabh, Roh, David, Mukherjee, Sugoto, Johnston, Karen C., Southerland, Andrew M., Mayer, Stephan A., Lignelli, Angela, Connolly, Edward Sander
• Format: Article
• Language: English
• ISSN: 0039-2499; 1524-4628
• DOI: 10.1161/STROKEAHA.119.026764

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;