摘要：Accurate lesion segmentation is critical in stroke rehabilitation research for the quantifcation of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise . We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1 .2, N = 304) to encourage the development of better algorithms . However, many methods developed with ATLAS v1 .2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the feld . Here we present ATLAS v2 .0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets . algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2 .0 will lead to improved algorithms, facilitating large-scale stroke research.
