Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury

Abstract

Background: White matter hyperintensities (WMHs) are foci of abnormal signal intensity in white matter regions seen with magnetic resonance imaging (MRI). WMHs are associated with normal ageing and have shown prognostic value in neurological conditions such as traumatic brain injury (TBI). The impracticality of manually quantifying these lesions limits their clinical utility and motivates the utilization of machine learning techniques for automated segmentation workflows.

Methods: This study develops a concatenated random forest framework with image features for segmenting WMHs in a TBI cohort. The framework is built upon the Advanced Normalization Tools (ANTs) and ANTsR toolkits. MR (3D FLAIR, T2- and T1-weighted) images from 24 service members and veterans scanned in the Chronic Effects of Neurotrauma Consortium’s (CENC) observational study were acquired. Manual annotations were employed for both training and evaluation using a leave-one-out strategy. Performance measures include sensitivity, positive predictive value, score and relative volume difference.

Results: Final average results were: sensitivity = 0.68 ± 0.38, positive predictive value = 0.51 ± 0.40, = 0.52 ± 0.36, relative volume difference = 43 ± 26%. In addition, three lesion size ranges are selected to illustrate the variation in performance with lesion size.

Conclusion: Paired with correlative outcome data, supervised learning methods may allow for identification of imaging features predictive of diagnosis and prognosis in individual TBI patients.

Keywords:

• Neuroimaging
• brain imaging
• magnetic resonance imaging
• machine learning
• random forest decision tree
• deep learning
• TBI

Acknowledgements

The authors wish to acknowledge all other members of the CENC Neuroimaging Steering Committee and CENC leadership (Drs. David X. Cifu, Ramon Diaz-Arrastia and Rick Williams) for their support. We also gratefully acknowledge the assistance of Tracy Nolen, Chris Siege and Kevin Wilson. We would also like to thank the study participants and their family members.

Declaration of interest

This project was jointly supported by the Department of Defense (W81XWH-13-2-0095), the US Department of Veterans Affairs (I01 CX001135 and I01 RX 002174), as well as USUHS Grant HU 0001-08-0001. The authors report no financial disclosures or conflicts of interest. The views expressed here are those of the authors and do not necessarily reflect the official policy of position of the Department of the Navy, Department of Defense, nor the US Government. This work was prepared as a part of official duties; Title 17 USC §105 provides that copyright protection under this title is not available for any work of the US Government. Title 17 USC §101 defines a US Government work as a work prepared by a military service member of employee of the US Government as part of that person’s official duties.