Evidence map›Paper›PMID 37291372›Full record

ArticleRadiological physics and technology2023

Accuracy of skull stripping in a single-contrast convolutional neural network model using eight-contrast magnetic resonance images.

Masami Goto, Yujiro Otsuka, Akifumi Hagiwara, Shohei Fujita, Masaaki Hori, Koji Kamagata, Shigeki Aoki, Osamu Abe, Hajime Sakamoto, Yasuaki Sakano and 2 more

Abstract read
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Article in Radiological physics and technology, 2023. 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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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Masami GotoDepartment of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan. m.goto.ql@juntendo.ac.jp.ORCID http://orcid.org/0000-0001-5029-1385
Yujiro OtsukaDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
Akifumi HagiwaraDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Shohei FujitaDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
Masaaki HoriDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
Koji KamagataDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
Shigeki AokiDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
Osamu AbeDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Hajime SakamotoDepartment of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Yasuaki SakanoDepartment of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Shinsuke KyogokuDepartment of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Hiroyuki DaidaDepartment of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In automated analyses of brain morphometry, skull stripping or brain extraction is a critical first step because it provides accurate spatial registration and signal-intensity normalization. Therefore, it is imperative to develop an ideal skull-stripping method in the field of brain image analysis. Previous reports have shown that convolutional neural network (CNN) method is better at skull stripping than non-CNN methods. We aimed to evaluate the accuracy of skull stripping in a single-contrast CNN model using eight-contrast magnetic resonance (MR) images. A total of 12 healthy participants and 12 patients with a clinical diagnosis of unilateral Sturge-Weber syndrome were included in our study. A 3-T MR imaging system and QRAPMASTER were used for data acquisition. We obtained eight-contrast images produced by post-processing T1, T2, and proton density (PD) maps. To evaluate the accuracy of skull stripping in our CNN method, gold-standard intracranial volume (ICV

Indexed as

BrainSkullHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingNeural Networks, ComputerBrain extractionConvolutional neural networkIntracranial volumeMagnetic resonance imageSkull stripping

Identifiers

PMID37291372

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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.