Evidence map›Paper›PMID 32477616›Full record

ArticleJournal of pathology informatics2020

EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images.

Sudhir Sornapudi, Jason Hagerty, R Joe Stanley, William V Stoecker, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shellaine R Frazier

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pathology informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.6field-weighted citation impact, top 8% of its field
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

8 citing papers in PubMed, 24 citations in OpenAlex.

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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 at 4 institutions in 1 country.

Sudhir SornapudiDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
Jason HagertyDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
R Joe StanleyDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
William V StoeckerStoecker and Associates, Rolla, MO, USA.
Rodney LongLister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, DHHS, Bethesda, MD, USA.
Sameer AntaniLister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, DHHS, Bethesda, MD, USA.
George ThomaLister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, DHHS, Bethesda, MD, USA.
Rosemary ZunaDepartment of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Shellaine R FrazierDepartment of Surgical Pathology, University of Missouri Hospitals and Clinics, Columbia, MO, USA.
Missouri University of Science and Technology · USNational Institutes of Health · USUniversity of Missouri Hospital · USUniversity of Oklahoma Health Sciences Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutomated pathology techniques for detecting cervical cancer at the premalignant stage have advantages for women in areas with limited medical resources.

methodsThis article presents EpithNet, a deep learning approach for the critical step of automated epithelium segmentation in digitized cervical histology images. EpithNet employs three regression networks of varying dimensions of image input blocks (patches) surrounding a given pixel, with all blocks at a fixed resolution, using varying network depth.

resultsThe proposed model was evaluated on 311 digitized histology epithelial images and the results indicate that the technique maximizes region-based information to improve pixel-wise probability estimates. EpithNet-mc model, formed by intermediate concatenation of the convolutional layers of the three models, was observed to achieve 94% Jaccard index (intersection over union) which is 26.4% higher than the benchmark model.

conclusionsEpithNet yields better epithelial segmentation results than state-of-the-art benchmark methods.

Indexed as

Cervical cancercervical intraepithelial neoplasiaconvolutional neural networkdeep learningimage processingsegmentation

Identifiers

PMID32477616
PMCPMC7245344
OpenAlexW3014754116

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

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.