ArticleFrontiers in computational neuroscience2020
Novel Volumetric Sub-region Segmentation in Brain Tumors.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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 synthesis or guideline pooled it.
- Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis.European radiology · 2021Pooled it
- High-precision brain tumor segmentation with switchable normalization in faster R-CNN architecture.Scientific reports · 2026Article
- Overview of Multi-Modal Brain Tumor MR Image Segmentation.Healthcare (Basel, Switzerland) · 2021Review
- Multi-Task Deep Supervision on Attention R2U-Net for Brain Tumor Segmentation.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A novel deep learning based model called Multi-Planar Spatial Convolutional Neural Network (MPS-CNN) is proposed for effective, automated segmentation of different sub-regions
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.