Evidence map›Paper›PMID 32132913›Full record

ArticleFrontiers in computational neuroscience2020

A Novel Approach for Fully Automatic Intra-Tumor Segmentation With 3D U-Net Architecture for Gliomas.

Ujjwal Baid, Sanjay Talbar, Swapnil Rane, Sudeep Gupta, Meenakshi H Thakur, Aliasgar Moiyadi, Nilesh Sable, Mayuresh Akolkar, Abhishek Mahajan

Abstract read
In one paragraph

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 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Comparing the diagnostic efficacy of [Frontiers in medicine · 2025
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  8. Leveraging 2D Deep Learning ImageNet-trained models for Native 3D Medical Image Analysis.Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) · 2023
    Article
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  18. Article
  19. Overview of Multi-Modal Brain Tumor MR Image Segmentation.Healthcare (Basel, Switzerland) · 2021
    Review
  20. Review
4 · The record

Corrections and comments

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

5 · Who and what money

Authors and funding

9 authors.

Ujjwal BaidDepartment of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India.
Sanjay TalbarDepartment of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India.
Swapnil RaneDepartment of Pathology, Tata Memorial Centre, Tata Memorial Hospital, Mumbai, India.
Sudeep GuptaDepartment of Medical Oncology, Tata Memorial Centre, Tata Memorial Hospital, Mumbai, India.
Meenakshi H ThakurDepartment of Radiodiagnosis and Imaging, Tata Memorial Centre, Tata Memorial Hospital, Mumbai, India.
Aliasgar MoiyadiDepartment of Neurosurgery Services, Tata Memorial Centre, Tata Memorial Hospital, Mumbai, India.
Nilesh SableDepartment of Radiodiagnosis and Imaging, Tata Memorial Centre, Tata Memorial Hospital, Mumbai, India.
Mayuresh AkolkarDepartment of Radiodiagnosis and Imaging, Tata Memorial Centre, Tata Memorial Hospital, Mumbai, India.
Abhishek MahajanDepartment of Radiodiagnosis and Imaging, Tata Memorial Centre, Tata Memorial Hospital, Mumbai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

3D U-Netconvolutional neural networkdeep learninggliomaintra-tumor segmentation

Identifiers

PMID32132913
PMCPMC7041417

What OpenQuestion holds

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Registered trials

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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.