A fast and accurate brain extraction method for CT head images.

Dingyuan Hu, Hongbin Liang, Shiya Qu, Chunyu Han, Yuhang Jiang

Abstract read

In one paragraph

Article in BMC medical imaging, 2023. 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

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

1 citing paper in PubMed.

Triplanar Point Cloud Reconstruction of Head Skin Surface from Computed Tomography Images in Markerless Image-Guided Surgery.Bioengineering (Basel, Switzerland) · 2025
Article

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

5 authors.

Dingyuan HuSchool of Mechanical Engineering and Automation, University of Science and Technology Liaoning, NO.185 in Qianshan Middle Street, Anshan, 114000, Liaoning Province, PR China.

Hongbin LiangSchool of Mechanical Engineering and Automation, University of Science and Technology Liaoning, NO.185 in Qianshan Middle Street, Anshan, 114000, Liaoning Province, PR China. lianghb@hit.edu.cn.

Shiya QuSchool of Mechanical Engineering and Automation, University of Science and Technology Liaoning, NO.185 in Qianshan Middle Street, Anshan, 114000, Liaoning Province, PR China.

Chunyu HanSchool of Mechanical Engineering and Automation, University of Science and Technology Liaoning, NO.185 in Qianshan Middle Street, Anshan, 114000, Liaoning Province, PR China.

Yuhang JiangSchool of Mechanical Engineering and Automation, University of Science and Technology Liaoning, NO.185 in Qianshan Middle Street, Anshan, 114000, Liaoning Province, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBrain extraction is an essential prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion recognition, location, and segmentation. Segmentation using a fully convolutional neural network (FCN) yields high accuracy but a relatively slow extraction speed.

methodsThis paper proposes an integrated algorithm, FABEM, to address the above issues. This method first uses threshold segmentation, closed operation, convolutional neural network (CNN), and image filling to generate a specific mask. Then, it detects the number of connected regions of the mask. If the number of connected regions equals 1, the extraction is done by directly multiplying with the original image. Otherwise, the mask was further segmented using the region growth method for original images with single-region brain distribution. Conversely, for images with multi-region brain distribution, Deeplabv3 + is used to adjust the mask. Finally, the mask is multiplied with the original image to complete the extraction.

resultsThe algorithm and 5 FCN models were tested on 24 datasets containing different lesions, and the algorithm's performance showed MPA = 0.9968, MIoU = 0.9936, and MBF = 0.9963, comparable to the Deeplabv3+. Still, its extraction speed is much faster than the Deeplabv3+. It can complete the brain extraction of a head CT image in about 0.43 s, about 3.8 times that of the Deeplabv3+.

conclusionThus, this method can achieve accurate brain extraction from head CT images faster, creating a good basis for subsequent brain volume measurement and feature extraction of intracranial lesions.

Indexed as

AlgorithmsBrainHumansNeural Networks, ComputerTomography, X-Ray ComputedBrain extractionFully convolutional neural networkHead CT imagesThreshold segmentation

Identifiers

PMID37700250

DOI10.1007/978-3-030-01234-2_49

PMCPMC10498619

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.