Kelly Payette

TL;DR: In this article, a publicly available dataset of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, deep grey matter) was introduced.

TL;DR: Larger total and selected regional postoperative brain volumes were found to be associated with better cognitive and language outcomes, independent of length of intensive care unit stay for total, cortical, temporal, frontal and cerebellar volumes.

TL;DR: In this paper, transfer learning with noisy multi-class labels is used to automatically segment high-resolution fetal brain MRIs using a single set of segmentations created with one reconstruction method and tested for generalizability across other reconstruction methods.

TL;DR: This work proposes using transfer learning with noisy multi-class labels to automatically segment high resolution fetal brain MRIs using a single set of seg-mentations created with one reconstruction method and tested for generalizability across other reconstruction methods.

TL;DR: In this paper , only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%), while 48% of respondents applied postprocessing steps.