Richard McKinley

TL;DR: This paper proposes a common evaluation framework for automatic stroke lesion segmentation from MRIP, describes the publicly available datasets, and presents the results of the two sub‐challenges: Sub‐Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES).

TL;DR: There is a cluster of four methods that rank significantly better than the other methods, with one clear winner, and the inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners.

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: This paper presented the largest FL study to date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature.