Fully Automated Segmentation Algorithm for Hematoma Volumetric Analysis in Spontaneous Intracerebral Hemorrhage.
Publication/Presentation Date
12-1-2019
Abstract
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 (
Volume
50
Issue
12
First Page
3416
Last Page
3423
ISSN
1524-4628
Published In/Presented At
Ironside, N., Chen, C. J., Mutasa, S., Sim, J. L., Marfatia, S., Roh, D., Ding, D., Mayer, S. A., Lignelli, A., & Connolly, E. S. (2019). Fully Automated Segmentation Algorithm for Hematoma Volumetric Analysis in Spontaneous Intracerebral Hemorrhage. Stroke, 50(12), 3416–3423. https://doi.org/10.1161/STROKEAHA.119.026561
Disciplines
Diagnosis | Medicine and Health Sciences | Other Analytical, Diagnostic and Therapeutic Techniques and Equipment | Radiology
PubMedID
31735138
Department(s)
Department of Radiology and Diagnostic Medical Imaging
Document Type
Article