RT Journal Article
SR Electronic
T1 A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 441451
DO 10.1101/441451
A1 Kaori L. Ito
A1 Hosung Kim
A1 Sook-Lei Liew
YR 2018
UL http://biorxiv.org/content/early/2018/10/11/441451.abstract
AB Accurate stroke lesion segmentation is a critical step in the neuroimaging processing pipeline to assess the relationship between post-stroke brain structure, function, and behavior. While many multimodal segmentation algorithms have been developed for acute stroke neuroimaging, few are effective with only a single T1-weighted (T1w) anatomical MRI. This is a critical gap because most stroke rehabilitation research relies on a single T1w MRI for defining the lesion. Although several attempts to automate the segmentation of chronic lesions on single-channel T1w MRI have been made, these approaches have not been systematically evaluated on a large dataset. Here, we performed an exhaustive review of the literature and identified one semi-and three fully automated approaches for segmentation of chronic stroke lesions using T1w MRI within the last ten years: Clusterize, Automated Lesion Identification, Gaussian naïve Bayes lesion detection, and LINDA. We evaluated each method on a large T1w stroke dataset (N=181) using visual and quantitative methods. LINDA was the most computationally expensive approach, but performed best across the three main evaluation metrics (median values: Dice Coefficient=0.50, Hausdorff’s Distance=36.34 mm, and Average Symmetric Surface Distance=4.97 mm), whereas the Gaussian Bayes method had the highest recall/least false positives (median=0.80). Segmentation accuracy in all automated methods were influenced by size (small: worst) and stroke territory (brainstem, cerebellum: worst) of the lesion. To facilitate reproducible science, we have made our analysis files publicly available online at https://github.com/npnl/elsa. We hope these findings are informative to future development of T1w lesion segmentation algorithms.