ArticleNeuroImage2014
Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation.
Article in NeuroImage, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it, 104 citations in OpenAlex.
- 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
- An end-to-end infant brain parcellation pipeline.Intelligent medicine · 2024Article
- Self-supervised learning with application for infant cerebellum segmentation and analysis.Nature communications · 2023Article
- An efficient memory reserving-and-fading strategy for vector quantization based 3D brain segmentation and tumor extraction using an unsupervised deep learning network.Cognitive neurodynamics · 2023Article
- 3D-MASNet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old infant brain MR images.Human brain mapping · 2023Article
- Role of deep learning in infant brain MRI analysis.Magnetic resonance imaging · 2019Review
- Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network.NeuroImage · 2019Article
- Radiological images and machine learning: Trends, perspectives, and prospects.Computers in biology and medicine · 2019Review
- 3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation.IEEE transactions on cybernetics · 2019Article
- Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge.IEEE transactions on medical imaging · 2019Article
- Computational neuroanatomy of baby brains: A review.NeuroImage · 2019Review
- Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts.NeuroImage · 2019Review
- Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans.IEEE transactions on bio-medical engineering · 2018Article
- Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism.Human brain mapping · 2018Article
- A Label-fusion-aided Convolutional Neural Network for Isointense Infant Brain Tissue Segmentation.Proceedings. IEEE International Symposium on Biomedical Imaging · 2018Article
- Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.International journal of computer assisted radiology and surgery · 2017Article
- Can we predict subject-specific dynamic cortical thickness maps during infancy from birth?Human brain mapping · 2017Article
- Combining a Patch-based Approach with a Non-rigid Registration-based Label Fusion Method for the Hippocampal Segmentation in Alzheimer's Disease.Neuroinformatics · 2017Article
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Corrections and comments
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Authors and funding
7 authors at 1 institution in 2 countries.
Funding
Abstract
Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination processes. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6-8months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889±0.008 for white matter and 0.870±0.006 for gray matter.
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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.