Evidence map›Paper›PMID 38686402›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2024

[Automatic detection method of intracranial aneurysms on maximum intensity projection images based on SE-CaraNet].

Peirui Bai, Xuefeng Song, Qingyi Liu, Jiahui Liu, Jin Cheng, Xiaona Xiu, Yande Ren, Chengjian Wang

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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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

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Peirui BaiSchool of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, P. R. China.
Xuefeng SongSchool of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, P. R. China.
Qingyi LiuSchool of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, P. R. China.
Jiahui LiuSchool of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, P. R. China.
Jin ChengSchool of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, P. R. China.
Xiaona XiuSchool of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, P. R. China.
Yande RenDepartment of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong 265000, P. R. China.
Chengjian WangDepartment of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong 265000, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conventional maximum intensity projection (MIP) images tend to ignore some morphological features in the detection of intracranial aneurysms, resulting in missed detection and misdetection. To solve this problem, a new method for intracranial aneurysm detection based on omni-directional MIP image is proposed in this paper. Firstly, the three-dimensional magnetic resonance angiography (MRA) images were projected with the maximum density in all directions to obtain the MIP images. Then, the region of intracranial aneurysm was prepositioned by matching filter. Finally, the Squeeze and Excitation (SE) module was used to improve the CaraNet model. Excitation and the improved model were used to detect the predetermined location in the omni-directional MIP image to determine whether there was intracranial aneurysm. In this paper, 245 cases of images were collected to test the proposed method. The results showed that the accuracy and specificity of the proposed method could reach 93.75% and 93.86%, respectively, significantly improved the detection performance of intracranial aneurysms in MIP images.

Indexed as

AlgorithmsImaging, Three-DimensionalIntracranial AneurysmMagnetic Resonance AngiographyHumansImage Processing, Computer-AssistedSensitivity and SpecificityCaraNetIntracranial aneurysm detectionMaximum intensity projectionSqueeze-and-Excitation module

Identifiers

PMID38686402
PMCPMC11058495

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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.