Automated segmentation of acute stroke lesions using a data-driven anomaly detection on diffusion weighted MRI

: Sanaz Nazari-Farsani, Mikko Nyman, Tomi Karjalainen, Marco Bucci, c, Janne Isojärvi, Lauri Nummenmaa

: 2020

Journal of Neuroscience Methods

: Journal of neuroscience methods

: J Neurosci Methods

: 108575

: 333

: 9

: 0165-0270

: 1872-678X

DOI: https://doi.org/10.1016/j.jneumeth.2019.108575

Successful delineation of lesions in acute ischemic strokes (AIS) is crucial for increasing the likelihood of good clinical outcome for the patient.\nWe developed a fully automated method to localize and segment AIS lesions in variable locations for 192 multimodal 3D-magnetic resonance images (MRI) including 106 stroke and 86 healthy cases. The method works based on the Crawford-Howell t-test and comparison of stroke images to healthy controls. We then developed a classifier to discriminate the images into stroke or non-stroke categories following the lesion segmentation.\nThe mean Dice similarity coefficient (DSC) for the test set was 0.50 ± 0.21 (min-max: 0.07-0.83) and mean net overlap was 0.66 ± 0.18 (min-max: 0.22-1). The experimental results for the classification of strokes from non-strokes showed mean accuracy, precision, sensitivity, and specificity of 73 %, 0.77 %, 84 %, and 69 %, respectively.\nThe performance of our methods is comparable with previously published approaches based on machine learning and/or deep learning lesion segmentation techniques. However, most of the previously published methods have yielded low sensitivity, are computationally heavy, and difficult to interpret. The present approach is a significant improvement because it does not require high computation power and memory and can be implemented on a desktop workstation and integrated into the routine clinical diagnostic pipeline.\nThe current method is straightforward, fast, and shows good agreement with the lesions identified by human experts.\nBACKGROUND\nNEW METHODS\nRESULTS\nCOMPARISON WITH EXISTING METHOD\nCONCLUSIONS

brain lesions, Brain Magnetic Resonance Imaging (MRI), Database, functional brain imaging, human brain, Lesion prediction, machine learning