ArticleIEEE transactions on medical imaging2019
Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it, 193 citations in OpenAlex.
- Deep learning techniques for isointense infant brain tissue segmentation: a systematic literature review.Frontiers in medicine · 2023Pooled it
- An Infant Brain MR Image Segmentation Method with Attention Mechanism, Multi-Resolution Deep Supervision and Class Weight Tuning Strategy.Journal of biomedical physics & engineering · 2026Article
- Retinal layer segmentation in OCT images with a 2.5D cross-slice feature fusion module for glaucoma assessment.Biomedical optics express · 2026Article
- Semi-automatic mask guidance enhances 3D tumor segmentation in medical imaging.Communications medicine · 2026Article
- Automatic lateral ventricle and choroid plexus segmentation method in infant brain MR images.BMC medical imaging · 2026Article
- 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
- White matter hyperintensity segmentation of multiple sclerosis and neuromyelitis optical spectrum disorders using 2.5D FrC-ResUnet.Quantitative imaging in medicine and surgery · 2025Article
- Deep infant brain segmentation from multi-contrast MRI.Conference record. Asilomar Conference on Signals, Systems & Computers · 2025Article
- 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
- A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks.Nature biomedical engineering · 2025Article
- 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
- Brain volume in infants with metopic synostosis: Less white matter volume with an accelerated growth pattern in early life.Journal of anatomy · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Functional connectome through the human life span.bioRxiv : the preprint server for biology · 2024Article
- VINNA for neonates: Orientation independence through latent augmentations.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- An end-to-end infant brain parcellation pipeline.Intelligent medicine · 2024Article
- A Novel Mis-Seg-Focus Loss Function Based on a Two-Stage nnU-Net Framework for Accurate Brain Tissue Segmentation.Bioengineering (Basel, Switzerland) · 2024Article
- Updates to the Melbourne Children's Regional Infant Brain Software Package (M-CRIB-S).Neuroinformatics · 2024Article
Corrections and comments
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Authors and funding
29 authors at 10 institutions in 8 countries.
Funding
Abstract
Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.
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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.