Evidence map›Paper›PMID 35916116›Full record

ArticleMedical physics2022

COVID-19 CT image segmentation based on improved Res2Net.

Shangwang Liu, Xiufang Tang, Tongbo Cai, Yangyang Zhang, Changgeng Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shangwang LiuSchool of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China.
Xiufang TangSchool of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China.
Tongbo CaiSchool of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China.
Yangyang ZhangSchool of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China.
Changgeng WangSchool of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China.

Funding

Henan Province 21A520022
6 · The paper itself

Abstract

purposeCorona virus disease 2019 (COVID-19) is threatening the health of the global people and bringing great losses to our economy and society. However, computed tomography (CT) image segmentation can make clinicians quickly identify the COVID-19-infected regions. Accurate segmentation infection area of COVID-19 can contribute screen confirmed cases.

methodsWe designed a segmentation network for COVID-19-infected regions in CT images. To begin with, multilayered features were extracted by the backbone network of Res2Net. Subsequently, edge features of the infected regions in the low-level feature f

resultsExperimental results show that, on the public COVID-19 dataset, the Dice, sensitivity, specificity,

conclusionsOur method performs well on COVID-19 CT image segmentation. We also find that our method is so portable that can be suitable for various current popular networks. In a word, our method can help screen people infected with COVID-19 effectively and save the labor power of clinicians and radiologists.

Indexed as

COVID-19HumansImage Processing, Computer-AssistedRadiologistsTomography, X-Ray Computedcontext explorationCOVID-19 CT image segmentationedge attentionRes2Net

Identifiers

PMID35916116
PMCPMC9538682

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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.