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Fully Automated Segmentation Algorithm for Perihematomal Edema Volumetry After Spontaneous Intracerebral Hemorrhage
Journal article   Peer reviewed

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

Natasha Ironside, Ching-Jen Chen, Simukayi Mutasa, Justin L. Sim, Dale Ding, Saurabh Marfatiah, David Roh, Sugoto Mukherjee, Karen C. Johnston, Andrew M. Southerland, …
Stroke, Vol.51(3)
2020

Abstract

Adult Algorithms Automation Biomarkers Brain Edema Cerebral Hemorrhage Female Hematoma Humans Image Processing, Computer-Assisted Machine Learning Male Middle Aged Neuroimaging Tomography, X-Ray Computed Treatment Outcome biological marker adult algorithm automation brain edema brain hemorrhage complication diagnostic imaging female hematoma human image processing machine learning male middle aged neuroimaging treatment outcome x-ray computed tomography
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. © 2020 Lippincott Williams and Wilkins. All rights reserved.

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