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Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images.

Publication ,  Journal Article
Moon, HS; Heffron, L; Mahzarnia, A; Obeng-Gyasi, B; Holbrook, M; Badea, CT; Feng, W; Badea, A
Published in: Magn Reson Imaging
October 2022

Magnetic resonance (MR) imaging (MRI) is commonly used to diagnose, assess and monitor stroke. Accurate and timely segmentation of stroke lesions provides the anatomico-structural information that can aid physicians in predicting prognosis, as well as in decision making and triaging for various rehabilitation strategies. To segment stroke lesions, MR protocols, including diffusion-weighted imaging (DWI) and T2-weighted fluid attenuated inversion recovery (FLAIR) are often utilized. These imaging sequences are usually acquired with different spatial resolutions due to time constraints. Within the same image, voxels may be anisotropic, with reduced resolution along slice direction for diffusion scans in particular. In this study, we evaluate the ability of 2D and 3D U-Net Convolutional Neural Network (CNN) architectures to segment ischemic stroke lesions using single contrast (DWI) and dual contrast images (T2w FLAIR and DWI). The predicted segmentations correlate with post-stroke motor outcome measured by the National Institutes of Health Stroke Scale (NIHSS) and Fugl-Meyer Upper Extremity (FM-UE) index based on the lesion loads overlapping the corticospinal tracts (CST), which is a neural substrate for motor movement and function. Although the four methods performed similarly, the 2D multimodal U-Net achieved the best results with a mean Dice of 0.737 (95% CI: 0.705, 0.769) and a relatively high correlation between the weighted lesion load and the NIHSS scores (both at baseline and at 90 days). A monotonically constrained quintic polynomial regression yielded R2 = 0.784 and 0.875 for weighted lesion load versus baseline and 90-Days NIHSS respectively, and better corrected Akaike information criterion (AICc) scores than those of the linear regression. In addition, using the quintic polynomial regression model to regress the weighted lesion load to the 90-Days FM-UE score results in an R2 of 0.570 with a better AICc score than that of the linear regression. Our results suggest that the multi-contrast information enhanced the accuracy of the segmentation and the prediction accuracy for upper extremity motor outcomes. Expanding the training dataset to include different types of stroke lesions and more data points will help add a temporal longitudinal aspect and increase the accuracy. Furthermore, adding patient-specific data may improve the inference about the relationship between imaging metrics and functional outcomes.

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Published In

Magn Reson Imaging

DOI

EISSN

1873-5894

Publication Date

October 2022

Volume

92

Start / End Page

45 / 57

Location

Netherlands

Related Subject Headings

  • Stroke
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Magnetic Resonance Imaging
  • Ischemic Stroke
  • Humans
  • Diffusion Magnetic Resonance Imaging
  • 3202 Clinical sciences
  • 1702 Cognitive Sciences
  • 1103 Clinical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Moon, H. S., Heffron, L., Mahzarnia, A., Obeng-Gyasi, B., Holbrook, M., Badea, C. T., … Badea, A. (2022). Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images. Magn Reson Imaging, 92, 45–57. https://doi.org/10.1016/j.mri.2022.06.001
Moon, Hae Sol, Lindsay Heffron, Ali Mahzarnia, Barnabas Obeng-Gyasi, Matthew Holbrook, Cristian T. Badea, Wuwei Feng, and Alexandra Badea. “Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images.Magn Reson Imaging 92 (October 2022): 45–57. https://doi.org/10.1016/j.mri.2022.06.001.
Moon HS, Heffron L, Mahzarnia A, Obeng-Gyasi B, Holbrook M, Badea CT, et al. Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images. Magn Reson Imaging. 2022 Oct;92:45–57.
Moon, Hae Sol, et al. “Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images.Magn Reson Imaging, vol. 92, Oct. 2022, pp. 45–57. Pubmed, doi:10.1016/j.mri.2022.06.001.
Moon HS, Heffron L, Mahzarnia A, Obeng-Gyasi B, Holbrook M, Badea CT, Feng W, Badea A. Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images. Magn Reson Imaging. 2022 Oct;92:45–57.
Journal cover image

Published In

Magn Reson Imaging

DOI

EISSN

1873-5894

Publication Date

October 2022

Volume

92

Start / End Page

45 / 57

Location

Netherlands

Related Subject Headings

  • Stroke
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Magnetic Resonance Imaging
  • Ischemic Stroke
  • Humans
  • Diffusion Magnetic Resonance Imaging
  • 3202 Clinical sciences
  • 1702 Cognitive Sciences
  • 1103 Clinical Sciences