ArticleHuman brain mapping2020
Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Physics-embedded CycleGAN for robust metabolite mapping in short-readout deuterium metabolic imaging.Magnetic resonance letters · 2026Article
- [Research progress on medical image dataset expansion methods].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment.Journal of digital imaging · 2022Article
- The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects.Human brain mapping · 2022Article
- A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI.Frontiers in neurology · 2022Article
- Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data.Scientific reports · 2021Article
- A method for mapping retinal images in early visual cortical areas.NeuroImage · 2021Article
- MNI-FTD templates, unbiased average templates of frontotemporal dementia variants.Scientific data · 2021Article
- Cortical connectivity in the face of congenital structural changes-A case of homozygous LAMC3 mutation.Brain and behavior · 2021Article
- Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Diverse Applications of Artificial Intelligence in Neuroradiology.Neuroimaging clinics of North America · 2020Review
- CAST: A multi-scale convolutional neural network based automated hippocampal subfield segmentation toolbox.NeuroImage · 2020Article
- Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks.Human brain mapping · 2020Article
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4 authors.
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Abstract
Neuroanatomical segmentation in magnetic resonance imaging (MRI) of the brain is a prerequisite for quantitative volume, thickness, and shape measurements, as well as an important intermediate step in many preprocessing pipelines. This work introduces a new highly accurate and versatile method based on 3D convolutional neural networks for the automatic segmentation of neuroanatomy in T1-weighted MRI. In combination with a deep 3D fully convolutional architecture, efficient linear registration-derived spatial priors are used to incorporate additional spatial context into the network. An aggressive data augmentation scheme using random elastic deformations is also used to regularize the networks, allowing for excellent performance even in cases where only limited labeled training data are available. Applied to hippocampus segmentation in an elderly population (mean Dice coefficient = 92.1%) and subcortical segmentation in a healthy adult population (mean Dice coefficient = 89.5%), we demonstrate new state-of-the-art accuracies and a high robustness to outliers. Further validation on a multistructure segmentation task in a scan-rescan dataset demonstrates accuracy (mean Dice coefficient = 86.6%) similar to the scan-rescan reliability of expert manual segmentations (mean Dice coefficient = 86.9%), and improved reliability compared to both expert manual segmentations and automated segmentations using FIRST. Furthermore, our method maintains a highly competitive runtime performance (e.g., requiring only 10 s for left/right hippocampal segmentation in 1 × 1 × 1 mm
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