ArticleFrontiers in neuroinformatics2022
Accurate segmentation of neonatal brain MRI with deep learning.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- BONBID-HIE 2023: Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy.IEEE transactions on medical imaging · 2026Article
- Research hotspots and trend of the heart-brain axis by MRI: a bibliometric analysis.Frontiers in cardiovascular medicine · 2026Article
- Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence.Pediatric research · 2025Review
- Development and Validation of an MRI-Based Brain Volumetry Model Predicting Poor Psychomotor Outcomes in Preterm Neonates.Journal of clinical medicine · 2025Article
- A review of applications of automated ventricular parcellation from magnetic resonance imaging of the brain.Frontiers in neurology · 2025Review
- Subject-specific atlas for automatic brain tissue segmentation of neonatal magnetic resonance images.Scientific reports · 2024Article
- Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.Journal of perinatology : official journal of the California Perinatal Association · 2024Review
- Multilabel SegSRGAN-A framework for parcellation and morphometry of preterm brain in MRI.PloS one · 2024Article
- Application of Deep Learning for Prediction of Alzheimer's Disease in PET/MR Imaging.Bioengineering (Basel, Switzerland) · 2023Review
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Authors and funding
2 authors.
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Abstract
An important step toward delivering an accurate connectome of the human brain is robust segmentation of 3D Magnetic Resonance Imaging (MRI) scans, which is particularly challenging when carried out on perinatal data. In this paper, we present an automated, deep learning-based pipeline for accurate segmentation of tissues from neonatal brain MRI and extend it by introducing an age prediction pathway. A major constraint to using deep learning techniques on developing brain data is the need to collect large numbers of ground truth labels. We therefore also investigate two practical approaches that can help alleviate the problem of label scarcity without loss of segmentation performance. First, we examine the efficiency of different strategies of distributing a limited budget of annotated 2D slices over 3D training images. In the second approach, we compare the segmentation performance of pre-trained models with different strategies of fine-tuning on a small subset of preterm infants. Our results indicate that distributing labels over a larger number of brain scans can improve segmentation performance. We also show that even partial fine-tuning can be superior in performance to a model trained from scratch, highlighting the relevance of transfer learning strategies under conditions of label scarcity. We illustrate our findings on large, publicly available T1- and T2-weighted MRI scans (
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