Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage

Authors

Rajat Dhar, From the Department of Neurology, Washington University School of Medicine, MO (R.D., Y.C., A.H., A.R., C.-L.P., J.-M.L.).
Guido J. Falcone, Department of Neurology (G.J.F., E.P.K., R.B.N., K.R., J.A., K.N.S.), Yale School of Medicine, New Haven, CT.
Yasheng Chen, From the Department of Neurology, Washington University School of Medicine, MO (R.D., Y.C., A.H., A.R., C.-L.P., J.-M.L.).
Ali Hamzehloo, From the Department of Neurology, Washington University School of Medicine, MO (R.D., Y.C., A.H., A.R., C.-L.P., J.-M.L.).
Elayna P. Kirsch, Department of Neurology (G.J.F., E.P.K., R.B.N., K.R., J.A., K.N.S.), Yale School of Medicine, New Haven, CT.
Rommell B. Noche, Department of Neurology (G.J.F., E.P.K., R.B.N., K.R., J.A., K.N.S.), Yale School of Medicine, New Haven, CT.
Kilian Roth, Department of Neurology (G.J.F., E.P.K., R.B.N., K.R., J.A., K.N.S.), Yale School of Medicine, New Haven, CT.
Julian Acosta, Department of Neurology (G.J.F., E.P.K., R.B.N., K.R., J.A., K.N.S.), Yale School of Medicine, New Haven, CT.
Andres Ruiz, From the Department of Neurology, Washington University School of Medicine, MO (R.D., Y.C., A.H., A.R., C.-L.P., J.-M.L.).
Chia-Ling Phuah, From the Department of Neurology, Washington University School of Medicine, MO (R.D., Y.C., A.H., A.R., C.-L.P., J.-M.L.).
Daniel Woo, Department of Neurology, University of Cincinnati, OH (D.W.).
Thomas M. Gill, Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT.
Kevin N. Sheth, Department of Neurology (G.J.F., E.P.K., R.B.N., K.R., J.A., K.N.S.), Yale School of Medicine, New Haven, CT.
Jin-Moo Lee, From the Department of Neurology, Washington University School of Medicine, MO (R.D., Y.C., A.H., A.R., C.-L.P., J.-M.L.).

Document Type

Article

Abstract

Background and Purpose- Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectively, in spontaneous intracerebral hemorrhage. An automated imaging pipeline capable of accurately and rapidly quantifying these biomarkers would facilitate large cohort studies evaluating underlying mechanisms of injury. Methods- Regions of hemorrhage and PHE were manually delineated on computed tomography scans of patients enrolled in 2 intracerebral hemorrhage studies. Manual ground-truth masks from the first cohort were used to train a fully convolutional neural network to segment images into hemorrhage and PHE. The primary outcome was automated-versus-human concordance in hemorrhage and PHE volumes. The secondary outcome was voxel-by-voxel overlap of segmentations, quantified by the Dice similarity coefficient (DSC). Algorithm performance was validated on 84 scans from the second study. Results- Two hundred twenty-four scans from 124 patients with supratentorial intracerebral hemorrhage were used for algorithm derivation. Median volumes were 18 mL (interquartile range, 8-43) for hemorrhage and 12 mL (interquartile range, 5-30) for PHE. Concordance was excellent (0.96) for automated quantification of hemorrhage and good (0.81) for PHE, with DSC of 0.90 (interquartile range, 0.85-0.93) and 0.54 (0.39-0.65), respectively. External validation confirmed algorithm accuracy for hemorrhage (concordance 0.98, DSC 0.90) and PHE (concordance 0.90, DSC 0.55). This was comparable with the consistency observed between 2 human raters (DSC 0.90 for hemorrhage, 0.57 for PHE). Conclusions- We have developed a deep learning-based imaging algorithm capable of accurately measuring hemorrhage and PHE volumes. Rapid and consistent automated biomarker quantification may accelerate powerful and precise studies of disease biology in large cohorts of intracerebral hemorrhage patients.

Medical Subject Headings

Algorithms; Brain Edema (complications); Cerebral Hemorrhage (complications); Cohort Studies; Deep Learning; Edema (complications); Female; Hematoma (complications); Humans; Male; Middle Aged

Publication Date

2-1-2020

Publication Title

Stroke

E-ISSN

1524-4628

Volume

51

Issue

2

First Page

648

Last Page

651

PubMed ID

31805845

Digital Object Identifier (DOI)

10.1161/STROKEAHA.119.027657

Recommended Citation

Dhar, Rajat; Falcone, Guido J.; Chen, Yasheng; Hamzehloo, Ali; Kirsch, Elayna P.; Noche, Rommell B.; Roth, Kilian; Acosta, Julian; Ruiz, Andres; Phuah, Chia-Ling; Woo, Daniel; Gill, Thomas M.; Sheth, Kevin N.; and Lee, Jin-Moo, "Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage" (2020). Neurology. 1718.
https://scholar.barrowneuro.org/neurology/1718