ArticleIEEE transactions on medical imaging2021
A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI.
Article 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 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Detailed Delineation of the Fetal Brain in Diffusion MRI via Multi-Task Learning.IEEE transactions on medical imaging · 2026Article
- Mechanics of the Spatiotemporal Evolution of Sulcal Pits in the Folding Brain.Human brain mapping · 2025Article
- Longitudinal magnetic resonance imaging reveals differences in cortical expansion in fetuses with congenital heart defects.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results.IEEE transactions on medical imaging · 2025Article
- Article
- Anatomically constrained tractography of the fetal brain.NeuroImage · 2024Article
- Advances in Fetal Brain Imaging.Magnetic resonance imaging clinics of North America · 2024Review
- A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images.European radiology · 2024Article
- The nnU-Net based method for automatic segmenting fetal brain tissues.Health information science and systems · 2023Article
- Artificial intelligence applied to fetal MRI: A scoping review of current research.The British journal of radiology · 2023Article
- Retrospective motion correction in foetal MRI for clinical applications: existing methods, applications and integration into clinical practice.The British journal of radiology · 2023Review
- BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI.bioRxiv : the preprint server for biology · 2023Article
- Learning to segment fetal brain tissue from noisy annotations.Medical image analysis · 2023Article
- Deep Learning-Based Multiclass Brain Tissue Segmentation in Fetal MRIs.Sensors (Basel, Switzerland) · 2023Article
- A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN).Scientific reports · 2022Article
- Medical Image Segmentation Using Transformer Networks.IEEE access : practical innovations, open solutions · 2022Article
- ESA-UNet for assisted diagnosis of cardiac magnetic resonance image based on the semantic segmentation of the heart.Frontiers in cardiovascular medicine · 2022Article
- MRF-Net: A multi-branch residual fusion network for fast and accurate whole-brain MRI segmentation.Frontiers in neuroscience · 2022Article
- Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children.Frontiers in computational neuroscience · 2021Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burden of manual refinement of segmentations, we have developed a new and powerful deep learning segmentation method. Our method exploits new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections. We evaluated our method quantitatively based on several performance measures and expert evaluations. Results show that our method outperforms several state-of-the-art deep models for segmentation, as well as a state-of-the-art multi-atlas segmentation technique. We achieved average Dice similarity coefficient of 0.87, average Hausdorff distance of 0.96 mm, and average symmetric surface difference of 0.28 mm on reconstructed fetal brain MRI scans of fetuses scanned in the gestational age range of 16 to 39 weeks (28.6± 5.3). With a computation time of less than 1 minute per fetal brain, our method can facilitate and accelerate large-scale studies on normal and altered fetal brain cortical maturation and folding.
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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.