ArticleNeuroinformatics2018
Field of View Normalization in Multi-Site Brain MRI.
Article in Neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.Frontiers in neuroscience · 2024Pooled it
- Maternal Childhood Neglect is Linked to Greater Infant Cortisol Levels and Larger Infant Limbic Volumes.Child maltreatment · 2026Article
- BONBID-HIE 2023: Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy.IEEE transactions on medical imaging · 2026Article
- BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy (BONBID-HIE): I. MRI and Lesion Labeling.Scientific data · 2025Article
- A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): initial benchmark results and comprehensive analysis.PeerJ. Computer science · 2025Article
- Deep learning of structural MRI predicts fluid, crystallized, and general intelligence.Scientific reports · 2024Article
- aXonica: A support package for MRI based Neuroimaging.Biotechnology notes (Amsterdam, Netherlands) · 2024Article
- A three-step, "brute-force" approach toward optimized affine spatial normalization.Frontiers in computational neuroscience · 2024Article
- Maternal Childhood Abuse Versus Neglect Associated with Differential Patterns of Infant Brain Development.Research on child and adolescent psychopathology · 2023Article
- Linking maternal disrupted interaction and infant limbic volumes: The role of infant cortisol output.Psychoneuroendocrinology · 2023Article
- Negative versus withdrawn maternal behavior: Differential associations with infant gray and white matter during the first 2 years of life.Human brain mapping · 2023Article
- Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age.Frontiers in aging neuroscience · 2023Article
- Maternal Childhood Maltreatment Is Associated With Lower Infant Gray Matter Volume and Amygdala Volume During the First Two Years of Life.Biological psychiatry global open science · 2022Article
- Deep Relation Learning for Regression and Its Application to Brain Age Estimation.IEEE transactions on medical imaging · 2022Article
- Tissue volume estimation and age prediction using rapid structural brain scans.Scientific reports · 2022Article
- Article
- Diagnosis of early mild cognitive impairment using a multiobjective optimization algorithm based on T1-MRI data.Scientific reports · 2022Article
- Global-Local Transformer for Brain Age Estimation.IEEE transactions on medical imaging · 2022Article
- Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan.Medical image analysis · 2021Article
- Voxelwise and Regional Brain Apparent Diffusion Coefficient Changes on MRI from Birth to 6 Years of Age.Radiology · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Multi-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs). Normalizing FOV is currently done manually or using ad hoc pre-processing steps, which do not always generalize well to multi-site diverse images. In this paper, we show that (a) a generic FOV normalization approach is possible in multi-site diverse images; we show experiments on images acquired from Philips, GE, Siemens scanners, from 1.0T, 1.5T, 3.0T field of strengths, and from subjects 0-90 years of ages; and (b) generic FOV normalization improves skull stripping accuracy and consistency for multiple skull stripping algorithms; we show this effect for 5 skull stripping algorithms including FSL's BET, AFNI's 3dSkullStrip, FreeSurfer's HWA, BrainSuite's BSE, and MASS. We have released our FOV normalization software at http://www.nitrc.org/projects/normalizefov .
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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.