ArticleHuman brain mapping2021
Automated segmentation of deep brain nuclei using convolutional neural networks and susceptibility weighted imaging.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Revisiting brain iron deficiency in restless legs syndrome using magnetic resonance imaging.NeuroImage. Clinical · 2022Pooled it
- Nigrostriatal imaging in patients with clinically uncertain Parkinsonian syndromes: a head-to-head comparison between SPECT, PET, and MRI.European journal of nuclear medicine and molecular imaging · 2026Article
- Localized Iron Deposition and Shape Changes of Cerebellar Dentate in Progressive Supranuclear Palsy Clinical Variants.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Quantitative MRI-based decision model for early-stage parkinsonism diagnosis: a pilot feasibility study.Neuroimage. Reports · 2025Article
- Towards an Informed Choice of Diffusion MRI Image Contrasts for Cerebellar Segmentation.Human brain mapping · 2025Article
- Abnormalities of iron homeostasis and the dopaminergic system in Tourette syndrome revealed by 7T MRI and PET.Brain communications · 2025Article
- Projected pooling loss for red nucleus segmentation with soft topology constraints.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Deep Learning-based Approach for Brainstem and Ventricular MR Planimetry: Application in Patients with Progressive Supranuclear Palsy.Radiology. Artificial intelligence · 2024Article
- Automated segmentation of midbrain nuclei using deep learning and multisequence MRI: A longitudinal study on iron accumulation with age.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- Morphometric imaging and quantitative susceptibility mapping as complementary tools in the diagnosis of parkinsonisms.European journal of neurology · 2022Article
- CAU-Net: A Deep Learning Method for Deep Gray Matter Nuclei Segmentation.Frontiers in neuroscience · 2022Article
- Automated Segmentation of Midbrain Structures in High-Resolution Susceptibility Maps Based on Convolutional Neural Network and Transfer Learning.Frontiers in neuroscience · 2022Article
- Automated segmentation of deep brain nuclei using convolutional neural networks and susceptibility weighted imaging.Human brain mapping · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of susceptibility-sensitive MRI techniques, such as susceptibility weighted imaging (SWI), has enabled accurate in vivo visualization and quantification of iron deposition within the human brain. Although previous approaches have been introduced to segment iron-rich brain regions, such as the substantia nigra, subthalamic nucleus, red nucleus, and dentate nucleus, these methods are largely unavailable and manual annotation remains the most used approach to label these regions. Furthermore, given their recent success in outperforming other segmentation approaches, convolutional neural networks (CNN) promise better performances. The aim of this study was thus to evaluate state-of-the-art CNN architectures for the labeling of deep brain nuclei from SW images. We implemented five CNN architectures and considered ensembles of these models. Furthermore, a multi-atlas segmentation model was included to provide a comparison not based on CNN. We evaluated two prediction strategies: individual prediction, where a model is trained independently for each region, and combined prediction, which simultaneously predicts multiple closely located regions. In the training dataset, all models performed with high accuracy with Dice coefficients ranging from 0.80 to 0.95. The regional SWI intensities and volumes from the models' labels were strongly correlated with those obtained from manual labels. Performances were reduced on the external dataset, but were higher or comparable to the intrarater reliability and most models achieved significantly better results compared to multi-atlas segmentation. CNNs can accurately capture the individual variability of deep brain nuclei and represent a highly useful tool for their segmentation from SW images.
Indexed as
Identifiers
What OpenQuestion holds
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.