Eloy Roura

TL;DR: The proposed automated WM lesion segmentation method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities, while still in the top‐rank (3rd position) when using only T1‐w and FLAIR modalities.

TL;DR: Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods, are still trailing human expertise on both detection and delineation criteria, and it is demonstrated that computing a statistically robust consensus of the algorithms performs closer tohuman expertise on one score (segmentation) although still trailing on detection scores.

TL;DR: The accuracy of 10 brain tissue segmentation methods is compared analyzing the effects of SCSF ground‐truth voxels on accuracy estimations.

TL;DR: A new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images is presented and implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities.

TL;DR: Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods, are still trailing human expertise on both detection and delineation criteria, and it is demonstrated that computing a statistically robust consensus of the algorithms performs closer tohuman expertise on one score (segmentation) although still trailing on detection scores.