ArticleAJNR. American journal of neuroradiology2022
Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach.
Article in AJNR. American journal of neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Prenatal serotonin reuptake inhibitor exposure and maternal depression symptoms are associated with altered fetal brain and placental development.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Observational
- Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.Pediatric radiology · 2026Review
- The COVID generation: the neurodevelopmental consequences of in-utero COVID-19 exposure.Brain, behavior, and immunity · 2026Article
- Prenatal antidepressant exposure is associated withFrontiers in neuroscience · 2026Article
- The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.Children (Basel, Switzerland) · 2025Review
- RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models.Journal of imaging informatics in medicine · 2025Article
- Comparative study of 2D vs. 3D AI-enhanced ultrasound for fetal crown-rump length evaluation in the first trimester.BMC pregnancy and childbirth · 2025Article
- Advancements in MRI application in the morphological development of fetal cerebral venous system: a review.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025Review
- Evaluating the effects of volume censoring on fetal functional connectivity.Scientific reports · 2025Article
- A Clinical Approach to Semiautomated Three-Dimensional Fetal Brain Biometry-Comparing the Strengths and Weaknesses of Two Diagnostic Tools: 5DCNS+Journal of clinical medicine · 2023Article
- A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images.Diagnostics (Basel, Switzerland) · 2023Article
- Self-Enhanced Mixed Attention Network for Three-Modal Images Few-Shot Semantic Segmentation.Sensors (Basel, Switzerland) · 2023Article
- Default mode network functional connectivity strength in utero and the association with fetal subcortical development.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging.World journal of clinical cases · 2023Review
- Systematic evaluation of head motion on resting-state functional connectivity MRI in the neonate.Human brain mapping · 2023Article
- Development of Gestational Age-Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning.AJNR. American journal of neuroradiology · 2023Article
- Magnetic resonance imaging of the monkey fetal brain in utero.Investigative magnetic resonance imaging · 2022Article
- Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics.Entropy (Basel, Switzerland) · 2022Article
- FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
Abstract
background and purposeMR imaging provides critical information about fetal brain growth and development. Currently, morphologic analysis primarily relies on manual segmentation, which is time-intensive and has limited repeatability. This work aimed to develop a deep learning-based automatic fetal brain segmentation method that provides improved accuracy and robustness compared with atlas-based methods. MATERIALS AND
methodsA total of 106 fetal MR imaging studies were acquired prospectively from fetuses between 23 and 39 weeks of gestation. We trained a deep learning model on the MR imaging scans of 65 healthy fetuses and compared its performance with a 4D atlas-based segmentation method using the Wilcoxon signed-rank test. The trained model was also evaluated on data from 41 fetuses diagnosed with congenital heart disease.
resultsThe proposed method showed high consistency with the manual segmentation, with an average Dice score of 0.897. It also demonstrated significantly improved performance (
conclusionsThe proposed deep learning method provides an efficient and reliable approach for fetal brain segmentation, which outperformed segmentation based on a 4D atlas and has been used in clinical and research settings.
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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.