A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling.
File(s)
Published version
Author(s)
Type
Conference Paper
Abstract
We developed an automated system based on deep neural networks for fast and sensitive 3D image segmentation of cortical gray matter from fetal brain MRI. The lack of extensive/publicly available annotations presented a key challenge, as large amounts of labeled data are typically required for training sensitive models with deep learning. To address this, we: (i) generated preliminary tissue labels using the {\em Draw-EM} algorithm, which uses Expectation-Maximization and was originally designed for tissue segmentation in the neonatal domain; and (ii) employed a human-in-the-loop approach, whereby an expert fetal imaging annotator assessed and refined the performance of the model. By using a hybrid approach that combined automatically generated labels with manual refinements by an expert, we amplified the utility of ground truth annotations while immensely reducing their cost (283 slices). The deep learning system was developed, refined, and validated on 249 3D T2-weighted scans obtained from the {\em Developing Human Connectome Project}’s fetal cohort, acquired at 3T. Analysis of the system showed that it is invariant to gestational age at scan, as it generalized well to a wide age range (21 � 38 weeks) despite variations in cortical morphology and intensity across the fetal distribution. It was also found to be invariant to intensities in regions surrounding the brain (amniotic fluid), which often present a major obstacle to the processing of neuroimaging data in the fetal domain.
Editor(s)
Arbel, Tal
Ayed, Ismail Ben
Bruijne, Marleen de
Descoteaux, Maxime
Lombaert, Hervé
Pal, Christopher
Date Issued
2020-07-06
Date Acceptance
2020-07-01
Citation
MIDL, 2020, 121, pp.241-261
Publisher
PMLR
Start Page
241
End Page
261
Journal / Book Title
MIDL
Volume
121
Copyright Statement
© The authors and PMLR 2021. MLResearchPress
License URL
Identifier
http://proceedings.mlr.press/v121/
Source
Proceedings of the Third Conference on Medical Imaging with Deep Learning
Start Date
2020-07-06
Coverage Spatial
Montreal, QC, Canada
Date Publish Online
2020-07-06