Multi-Modal Segmentation of 3D Brain Scans Using Neural Networks.

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

4 citing papers in PubMed.

1. LYNSU: automated 3D neuropil segmentation of fluorescent images forFrontiers in neuroinformatics · 2024
   Article
2. Deep learning-based segmentation of brain parenchyma and ventricular system in CT scans in the presence of anomalies.Frontiers in neuroimaging · 2023
   Article
3. Higher-resolution quantification of white matter hypointensities by large-scale transfer learning from 2D images on the JPSC-AD cohort.Human brain mapping · 2022
   Article
4. COVID-19 diagnosis using deep learning neural networks applied to CT images.Frontiers in artificial intelligence · 2022
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

4 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

Indexed as