| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 6036817 | NeuroImage | 2009 | 5 Pages |
Abstract
Automated morphological segmentation combined with an adaptive boosting statistical classifier showed substantial agreement with manual segmentation, with an intraclass correlation coefficient (ICC) of 0.90 (95% confidence interval [CI], 0.80-0.95) for WMH volume and median similarity index (SI) of 0.58 (interquartile range [IQR] 0.50-0.65). The method also showed similarly high levels of agreement with semi-automated segmentation, with ICC 0.92 (95% CI 0.89-0.93) and median SI 0.56 (IQR 0.49-0.66). Its best performance was observed for the highest tertile of WMH volume. Threshold-based and Gaussian mixture model-driven automated segmentation generally did not perform well in this study.
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Authors
Richard Beare, Velandai Srikanth, Jian Chen, Thanh G. Phan, Jennifer Stapleton, Rebecca Lipshut, David Reutens,