ReviewMagnetic resonance imaging2019
Role of deep learning in infant brain MRI analysis.
Review in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- BIBSNet: A deep learning baby image brain segmentation network for MRI scans.Developmental cognitive neuroscience · 2026Article
- The impact of small for gestational age on visual-motor integration in preterm infants: insights from early brain magnetic resonance imaging.Translational pediatrics · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Enhanced U-Net for Infant Brain MRI Segmentation: A (2+1)D Convolutional Approach.Sensors (Basel, Switzerland) · 2025Article
- Convolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image segmentation.PloS one · 2025Article
- Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol.Developmental cognitive neuroscience · 2024Article
- Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset.Scientific reports · 2024Article
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting.NPJ digital medicine · 2023Review
- 3D-MASNet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old infant brain MR images.Human brain mapping · 2023Article
- Learning Strategies for Contrast-agnostic Segmentation via SynthSeg for Infant MRI data.Proceedings of machine learning research · 2022Article
- Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks.Stem cells international · 2022Article
- Quantitative Structural Brain Magnetic Resonance Imaging Analyses: Methodological Overview and Application to Rett Syndrome.Frontiers in neuroscience · 2022Review
- Deep Learning-Based Studies on Pediatric Brain Tumors Imaging: Narrative Review of Techniques and Challenges.Brain sciences · 2021Review
- MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances.Journal of magnetic resonance imaging : JMRI · 2021Review
- MhURI:A Supervised Segmentation Approach to Leverage Salient Brain Tissues in Magnetic Resonance Images.Computer methods and programs in biomedicine · 2021Article
- Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants.Frontiers in neuroscience · 2021Article
- Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children.Frontiers in computational neuroscience · 2021Article
- The Neurodevelopment of Autism from Infancy Through Toddlerhood.Neuroimaging clinics of North America · 2020Review
- A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis.PloS one · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relatively few methods have been applied to infant MRI data due numerous inherent challenges such as inhomogenous tissue appearance across the image, considerable image intensity variability across the first year of life, and a low signal to noise setting. This paper presents methods addressing these challenges in two selected applications, specifically infant brain tissue segmentation at the isointense stage and presymptomatic disease prediction in neurodevelopmental disorders. Corresponding methods are reviewed and compared, and open issues are identified, namely low data size restrictions, class imbalance problems, and lack of interpretation of the resulting deep learning solutions. We discuss how existing solutions can be adapted to approach these issues as well as how generative models seem to be a particularly strong contender to address them.
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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.