Evidence map›Paper›PMID 33898019›Full record

ArticleHealth information science and systems2021

Automatic breast tissue segmentation in MRIs with morphology snake and deep denoiser training via extended Stein's unbiased risk estimator.

Xiao-Xia Yin, Yunxiang Jian, Yang Zhang, Yanchun Zhang, Jianlin Wu, Hui Lu, Min-Ying Su

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Article in Health information science and systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xiao-Xia YinCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China.
Yunxiang JianCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China.
Yang ZhangTu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA USA.
Yanchun ZhangCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China.
Jianlin WuDepartment of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning China.
Hui LuCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China.
Min-Ying SuTu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA USA.

Funding

Univ.of Calif., Irvine Cancer Center Support GrantP30CA062203 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI Melanie Funes · 1994 to 2026
$57.9M
NCI NIH HHS P30 CA062203
6 · The paper itself

Abstract

Accurate segmentation of the breast tissue is a significant challenge in the analysis of breast MR images, especially analysis of breast images with low contrast. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI). Especially, when tumour is imaged for scanning, how to isolate the tumour region from chest will directly affect the accuracy for tumour to be detected. Due to low intensity levels and the close connection between breast and chest portion in MRIs, this study proposes an innovative, fully automatic and fast segmentation approach which combines histogram with inverse Gaussian gradient for morphology snakes, along with extended Stein's unbiased risk estimator (eSURE) applied for unsupervised learning of deep neural network Gaussian denoisers, aimed at accurate identification of landmarks such as chest and breast.

Indexed as

Adaptive histogram equalizationBreast cancerExtended Stein’s unbiased risk estimatorImage segmentationInverse Gaussian gradientMorphology snakesMRI

Identifiers

PMID33898019
PMCPMC8021687

What Socratic 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 Socratic graph.