ArticleNeuroImage. Clinical2019
Automatic extraction of the intracranial volume in fetal and neonatal MR scans using convolutional neural networks.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.Pediatric radiology · 2026Review
- Neuromelanin imaging outperforms free water imaging in diagnosing early Parkinson's disease: a comparative MRI study.NPJ Parkinson's disease · 2026Article
- Deep learning-based automated detection of fetal corpus callosum abnormalities in prenatal ultrasound.Frontiers in pediatrics · 2026Article
- Fetal Assessment Suite (FetAS): a web-based platform for automatic fetal MRI analysis using AI.Scientific reports · 2025Article
- SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI.IEEE transactions on medical imaging · 2024Article
- Review
- Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset.Scientific reports · 2024Article
- Fetal-BET: Brain Extraction Tool for Fetal MRI.IEEE open journal of engineering in medicine and biology · 2024Article
- What is the future of artificial intelligence in obstetrics? A qualitative study among healthcare professionals.BMJ open · 2023Article
- Accuracy of skull stripping in a single-contrast convolutional neural network model using eight-contrast magnetic resonance images.Radiological physics and technology · 2023Article
- Artificial intelligence applied to fetal MRI: A scoping review of current research.The British journal of radiology · 2023Article
- Applications of artificial intelligence in obstetrics.Ultrasonography (Seoul, Korea) · 2023Article
- Quantitative Structural Brain Magnetic Resonance Imaging Analyses: Methodological Overview and Application to Rett Syndrome.Frontiers in neuroscience · 2022Review
- Semi-automatic segmentation of the fetal brain from magnetic resonance imaging.Frontiers in neuroscience · 2022Article
- FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net.Frontiers in neuroscience · 2022Article
- Rapid head-pose detection for automated slice prescription of fetal-brain MRI.International journal of imaging systems and technology · 2021Article
- Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation.Sensors (Basel, Switzerland) · 2021Article
- Accurate Estimation of Total Intracranial Volume in MRI using a Multi-tasked Image-to-Image Translation Network.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MR images of infants and fetuses allow non-invasive analysis of the brain. Quantitative analysis of brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). Fast changes in the size and morphology of the developing brain, motion artifacts, and large variation in the field of view make ICV segmentation a challenging task. We propose an automatic method for segmentation of the ICV in fetal and neonatal MRI scans. The method was developed and tested with a diverse set of scans regarding image acquisition parameters (i.e. field strength, image acquisition plane, image resolution), infant age (23-45 weeks post menstrual age), and pathology (posthaemorrhagic ventricular dilatation, stroke, asphyxia, and Down syndrome). The results demonstrate that the method achieves accurate segmentation with a Dice coefficient (DC) ranging from 0.98 to 0.99 in neonatal and fetal scans regardless of image acquisition parameters or patient characteristics. Hence, the algorithm provides a generic tool for segmentation of the ICV that may be used as a preprocessing step for brain tissue segmentation in fetal and neonatal brain MR scans.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.