  To better understand early brain growth patterns in health and disorder, it
is critical to accurately segment infant brain magnetic resonance (MR) images
into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep
learning-based methods have achieved state-of-the-art performance; however, one
of major limitations is that the learning-based methods may suffer from the
multi-site issue, that is, the models trained on a dataset from one site may
not be applicable to the datasets acquired from other sites with different
imaging protocols/scanners. To promote methodological development in the
community, iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of
6-month infant subjects from multiple sites with different protocols/scanners
for the participating methods. Training/validation subjects are from UNC (MAP)
and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory
University. By the time of writing, there are 30 automatic segmentation methods
participating in iSeg-2019. We review the 8 top-ranked teams by detailing their
pipelines/implementations, presenting experimental results and evaluating
performance in terms of the whole brain, regions of interest, and gyral
landmark curves. We also discuss their limitations and possible future
directions for the multi-site issue. We hope that the multi-site dataset in
iSeg-2019 and this review article will attract more researchers on the
multi-site issue.
