ArticleFrontiers in neuroscience2021
Harmonized Segmentation of Neonatal Brain MRI.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Harmonization of slice thickness through resampling improves comparability of MRI-derived neonatal brain volumes.Frontiers in radiology · 2026Article
- Deep learning for the harmonization of structural MRI scans: a survey.Biomedical engineering online · 2024Review
- Subject-specific atlas for automatic brain tissue segmentation of neonatal magnetic resonance images.Scientific reports · 2024Article
- Multilabel SegSRGAN-A framework for parcellation and morphometry of preterm brain in MRI.PloS one · 2024Article
- BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI.bioRxiv : the preprint server for biology · 2023Article
- Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.Journal of digital imaging · 2023Review
- The Developing Human Connectome Project Neonatal Data Release.Frontiers in neuroscience · 2022Article
- Neuroimaging in the term newborn with neonatal encephalopathy.Seminars in fetal & neonatal medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Deep learning based medical image segmentation has shown great potential in becoming a key part of the clinical analysis pipeline. However, many of these models rely on the assumption that the train and test data come from the same distribution. This means that such methods cannot guarantee high quality predictions when the source and target domains are dissimilar due to different acquisition protocols, or biases in patient cohorts. Recently, unsupervised domain adaptation techniques have shown great potential in alleviating this problem by minimizing the shift between the source and target distributions, without requiring the use of labeled data in the target domain. In this work, we aim to predict tissue segmentation maps on
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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.