Corresponding author: Kaori L Ito (
Academic editor:
In stroke neuroimaging research, robust lesion quantification is important for performing statistical analyses relating brain changes to behavior after stroke. Specific lesion characteristics (e.g., stroke volume and location) can provide information about inter-individual differences in post-stroke neuroanatomy. However, as conventional practice for lesion segmentation involves manually tracing lesions from structural brain images, potential inconsistencies can arise due to subjective differences in the way lesions are defined. This would be particularly problematic for large, multisite collaborative efforts that involve many different individuals segmenting lesions. Thus, we aimed to develop a robust, user-friendly toolbox to improve standardization of lesion segmentation and to capture lesion characteristics for statistical analysis across individuals.
The SRQL toolbox incorporates three important features to make lesion analyses more robust: (1) a semi-automated white matter intensity correction, (2) a report of descriptive statistics on lesions, and (3) an option to perform analyses in native or standard space.
A semi-automated white matter intensity correction was implemented to remove healthy white matter voxels that may have accidentally been included in manual lesion segmentations (see Fig.
For each individual subject, a white matter mask is manually created in addition to the lesion segmentation. The white matter mask is a small circular region of interest (ROI) manually drawn on three contiguous slices within the healthy white matter of the contralesional hemisphere. Both the white matter and lesion masks are then converted into binarized files. The binarized white matter mask is overlayed onto the subject's T1 structural file to obtain a mean and standard deviation of the image intensity value within the region of the white matter mask. Next, the intensity of the subject's T1 structural file is applied onto the binarized lesion mask to create a T1-weighted lesion mask. The value of the mean plus or minus one standard deviation of the white matter intensity is calculated, and applied as threshold values on the T1-weighted lesion mask. This removes voxels within the lesion mask that are within the normal intensity range of healthy white matter for that individual. Lastly, the white matter adjusted lesion mask within the subject's file is binarized as the final lesion mask for that subject.
We first tested the toolbox on a single stroke subject's anatomical scan (Fig.
We then tested the SRQL toolbox on datasets from two different sites consisting of sixty subjects in total. Our script successfully ran on the two datasets, and we manually checked the hemispheric categorization of each lesion. We also checked the total number of voxels removed from the original lesion volume on average, which was 4.19±12.19%. Put another way, for each brain, there were approximately 4% of voxels that fell within the healthy white matter range that were subsequently removed.
We developed a toolbox to optimize quantification of lesions across research sites. The toolbox offers a robust analysis pipeline for performing lesion analyses across multiple datasets. Specifically, this toolbox improves the performance of statistical analysis on lesions through standardizing lesion masks with white matter adjustment, reporting descriptive lesion statistics, and normalizing adjusted lesion masks to standard space.
Future directions for the toolbox include: (1) automating the creation of each subject's white matter mask to decrease intensive labor demands and (2) constructing a report of group statistics on lesion data as well as a group probability map for visual identification of lesions across individuals.
The authors would like to thank the organizers and attendees of Brainhack LA, our research participants, and collaborators for their contributions.
White Matter Correction Pipeline; WM = white matter; SD= standard deviation
We tested our toolbox on a mock lesion mask. A. The stroke subject's T1 anatomical scan; B. The mock lesion mask is the red sphere; the blue mask is the lesion segmentation. The white matter was intentionally covered within the mock lesion mask, but as shown here, white matter voxels are removed by the white matter correction.