ReviewIEEE transactions on medical imaging2021
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge.
Review in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Developmental Brain Age Estimation From MRI Data: A Systematic Review of Deep Learning Approaches and Open Datasets.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
- Deep learning techniques for isointense infant brain tissue segmentation: a systematic literature review.Frontiers in medicine · 2023Pooled it
- miniMORPH: A Morphometry Pipeline for Low-Field MRI in Infants.Human brain mapping · 2026Article
- Increased risk of neurodevelopmental impairment associated with reduced brain volume at term-equivalent age in preterm infants with germinal matrix hemorrhage.Neuroradiology · 2026Article
- Brain foundation models with hypergraph dynamic adapter for brain disease analysis.Pattern recognition · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- A Novel Fixel-Based Approach for Resolving Neonatal White Matter Microstructure from Clinical Diffusion MRI.medRxiv : the preprint server for health sciences · 2026Article
- A novel fixel-based approach for resolving neonatal white matter microstructure from clinical diffusion MRI.Frontiers in neuroscience · 2026Article
- GLAC-Unet: Global-Local Active Contour Loss with an Efficient U-Shaped Architecture for Multiclass Medical Image Segmentation.Journal of imaging informatics in medicine · 2025Article
- Baby Open Brains: An open-source dataset of infant brain segmentations.Scientific data · 2025Article
- Artificial intelligence advances skull stripping across lifespan.Nature biomedical engineering · 2025Article
- A lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases.Nature biomedical engineering · 2025Article
- A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks.Nature biomedical engineering · 2025Article
- Disentangled latent energy-based style translation: An image-level structural MRI harmonization framework.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results.IEEE transactions on medical imaging · 2025Article
- Enhanced U-Net for Infant Brain MRI Segmentation: A (2+1)D Convolutional Approach.Sensors (Basel, Switzerland) · 2025Article
- Brain volume in infants with metopic synostosis: Less white matter volume with an accelerated growth pattern in early life.Journal of anatomy · 2024Article
- Baby Open Brains: An Open-Source Repository of Infant Brain Segmentations.bioRxiv : the preprint server for biology · 2024Article
- MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2024Article
- Assessing brain maturation on neonatal MRI-do we need a more quantitative approach?European radiology · 2024Article
Corrections and comments
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Authors and funding
35 authors.
Funding
Abstract
To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h owever, one of the major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, the iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. T raining/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participated in the iSeg-2019. In this article, 8 top-ranked methods were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves. We further pointed out their limitations and possible directions for addressing the multi-site issue. We find that multi-site consistency is still an open issue. We hope that the multi-site dataset in the iSeg-2019 and this review article will attract more researchers to address the challenging and critical multi-site issue in practice.
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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.