Automated brain extraction and immersive exploration of its layers in virtual reality for the rhesus macaque MRI data sets

Luo, Yanlin; Gao, Bin; Deng, Yiyi; Zhu, Xiaoming; Jiang, Tianzi; Zhao, Xudong; Yang, Zhengyi

doi:10.1002/cav.1841

Cited by 12 publications

(10 citation statements)

References 32 publications

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“…e main research direction of scene interactive animation is to count the running data from the observation point to the target, the displacement form, and the decent relative reference value based on the virtual picture. Luo et al analyzed the algorithm of the 3D tuple expected target model based on the GSP technology in the stable operation stage, did not adjust the speed, and proposed the immersive space and the Internet of things vision theory [6]. Colleen and Jones used the spatial distribution data that GSP keeps running at the power frequency, combined with GIS network analysis to determine the optimal configuration of service facilities [7].…”

Section: Introductionmentioning

confidence: 99%

Design and Research of Interactive Animation of Immersive Space Scene Based on Computer Vision Technology

Liu

et al. 2021

Mathematical Problems in Engineering

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With the development of computational simulation technology, the need for stable and immersive display effects of space scene animation in the field of life experience and visual art has gradually increased. In this case, the requirements for immersive characteristics of space scene animation have also been strengthened. The existing 3D space scene animation has a limited degree of stereoscopic display model and data visualization. However, the current space scene interactive animation adopts a plane layout as a whole, and the size of the view interface is generally fixed, including the size of virtual elements. However, the immersion in this paper interactive animation of spatial scene can effectively solve the problems of incomplete display of 3D effects and unstable view simulation in 3D effects. This paper takes immersive space scene animation as the research object and studies the 3D and characteristics of space scene animation based on computer parallel computing in different immersive space scenes and different virtual space technologies, as well as the animation effects of different scene transformations and art forms. The result of research shows that, with the continuous increase of the degree of virtuality within a certain range, the immersive effect of spatial scene interactive animation gradually becomes better. When the color of the space scene animation is below 15, the virtual immersive effect changes less. When the space scene is in the range of 15–20, the space scene will make people feel the atmosphere of a beautiful and mysterious illusion.

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Section: Introductionmentioning

confidence: 99%

Design and Research of Interactive Animation of Immersive Space Scene Based on Computer Vision Technology

Liu

et al. 2021

Mathematical Problems in Engineering

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“…First, two methods for monkey brain extraction have been incorporated to the toolbox. One is a modified version of the Brain Extraction Tool (BET) method (Luo et al, 2018); the other is based on multi-atlas segmentation (MaS). Second, the SPM registration method used for human brain has been replaced by methods based on Advanced Normalization Tools (ANTs) 1 (Avants et al, 2011), which are more suitable for monkey brain processing.…”

Section: Framework Of Monkeycbp Overviewmentioning

confidence: 99%

“…We have reported a BET (Smith, 2002) variation for rhesus brain extraction (Luo et al, 2018). Hereby, we briefly review a subset of the atlas/template-based and hybrid methods, in which multiple atlases are used.…”

Section: Brain Extractionmentioning

confidence: 99%

MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain

Yang

Fan

et al. 2020

Front. Neuroinform.

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Non-human primate models are widely used in studying the brain mechanism underlying brain development, cognitive functions, and psychiatric disorders. Neuroimaging techniques, such as magnetic resonance imaging, play an important role in the examinations of brain structure and functions. As an indispensable tool for brain imaging data analysis, brain atlases have been extensively investigated, and a variety of versions constructed. These atlases diverge in the criteria based on which they are plotted. The criteria range from cytoarchitectonic features, neurotransmitter receptor distributions, myelination fingerprints, and transcriptomic patterns to structural and functional connectomic profiles. Among them, brainnetome atlas is tightly related to brain connectome information and built by parcellating the brain on the basis of the anatomical connectivity profiles derived from structural neuroimaging data. The pipeline for building the brainnetome atlas has been published as a toolbox named ATPP (A Pipeline for Automatic Tractography-Based Brain Parcellation). In this paper, we present a variation of ATPP, which is dedicated to monkey brain parcellation, to address the significant differences in the process between the two species. The new toolbox, MonkeyCBP, has major alterations in three aspects: brain extraction, image registration, and validity indices. By parcellating two different brain regions (posterior cingulate cortex) and (frontal pole) of the rhesus monkey, we demonstrate the efficacy of these alterations. The toolbox has been made public (https://github.com/bheAI/MonkeyCBP_CLI, https: //github.com/bheAI/MonkeyCBP_GUI). It is expected that the toolbox can benefit the non-human primate neuroimaging community with high-throughput computation and low labor involvement.

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“…Since the development of the brain extraction research, a lot of automatic methods have been proposed. These methods can be divided into the classic-based [1][2][3][4][5][6][7], the atlas-based [8][9], and the learning-based [10][11][12][13][14][15][16][17][18][19][20]. Some classic automatic methods are yet widely used because of their advantages in high calculation speed and batch processing of data.…”

Section: Introductionmentioning

confidence: 99%

“…BET has stable performance and can handle some complex areas in brain MRI image well such as eyeballs. Therefore, many researchers have proposed improved brain extraction methods based on BET [2][3][4]. However, these classical methods are highly dependent on parameter setting, and their extraction accuracy is not enough to meet clinical requirements.…”

Section: Introductionmentioning

confidence: 99%

Brain Extraction From Brain MRI Images Based on Wasserstein GAN and O-Net

Jiang

Guo

Cheng³

et al. 2021

IEEE Access

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Brain extraction is an essential pre-processing step for neuroimaging analysis. It is difficult to achieve high-precision extraction from low-quality brain MRI images with artifacts and gray inconsistencies which often result in irregular hole regions in the extracted brain tissues. In addition, the U-Net based brain extraction methods trend to output over-smoothed brain boundary. To remove those irregular holes in the extracted mask, we proposed a new U-Net based model for brain extraction named O-Net. O-Net replaces the skip-connection path in the U-Net with a dual shortcut paths including an attention module to form an Oshaped network, which uses deep semantic information to highlight the target area while retaining more image details. O-Net effectively reduces the impact of intensity differences resulted from artifacts or gray inconsistencies in the brain MRI images on the extraction results. To obtain more precise brain boundary, we designed a new GAN based brain extraction method, which used above O-Net as the segmentation network. The discriminant network of the proposed GAN model adopts the residual structure to enhance the nonlinear expression ability of the network to balance the adversarial training of the two networks. To speed up the convergence of the proposed model, we added a segmentation loss to the adversarial loss to supervise the feature learning of the segmentation network. This method was compared with other popular brain extraction methods on two public datasets (IBSR18 and LPBA40). The mean dice similarity coefficients obtained by the proposed method were 97.26% and 98.29% on IBSR18 and LPBA40 respectively. The results of the proposed method are the best on the two public datasets. Experimental results prove that the proposed model can stably output high-precision brain tissue and reduce the influence of artifacts and gray inconsistencies.

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Automated brain extraction and immersive exploration of its layers in virtual reality for the rhesus macaque MRI data sets

Cited by 12 publications

References 32 publications

Design and Research of Interactive Animation of Immersive Space Scene Based on Computer Vision Technology

Design and Research of Interactive Animation of Immersive Space Scene Based on Computer Vision Technology

MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain

Brain Extraction From Brain MRI Images Based on Wasserstein GAN and O-Net

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