Atlas-Based Brain Extraction Is Robust Across RAT MRI Studies

MacNicol, Eilidh; Ćirić, Rastko; Kim, Eugene; Censo, Davide Di; Cash, Diana; Poldrack, Russell A.; Estéban, Oscar

doi:10.1109/isbi48211.2021.9433884

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…This integration provides fMRIPrep users with flexibility to spatially normalize their data to any template available in the Archive (see Box 2). This integration has also enabled the development of fMRIPrep adaptations, for instance to pediatric populations or rodent imaging ( MacNicol et al, 2021 ), using suitable templates from the archive. The uniform interface provided by the BIDS-like directory organisation and metadata enables straightforward integration of new templates into workflows equipped to use TemplateFlow templates.…”

Section: Figurementioning

confidence: 99%

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TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

Ćirić

Thompson

Lorenz

et al. 2021

Preprint

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Neuroimaging templates and corresponding atlases play a central role in experimental workflows and are the foundation for reporting of results. The proliferation of templates and atlases is one relevant source of methodological variability across studies, which has been brought to attention recently as an important challenge to reproducibility in neuroscience. Unclear nomenclature, an overabundance of template variants and options, inadequate provenance tracking and maintenance, and poor concordance between atlases introduce further unreliability into reported results. We introduce TemplateFlow, a cloud-based repository of human and nonhuman brain templates paired with a client application for programmatically accessing resources. TemplateFlow is designed to be extensible, providing a transparent pathway for researchers to contribute and vet templates and their associated atlases. Following software engineering best practices, TemplateFlow leverages technologies for unambiguous resource identification, data management, versioning and synchronisation, programmatic extensibility, and continuous integration. By equipping researchers with a robust resource for using and evaluating brain templates, TemplateFlow will contribute to increasing the reliability of neuroimaging results.

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Section: Figurementioning

confidence: 99%

“…An example template_description.json metadata file is displayed at the left for MNIPediatricAsym . In addition to general template metadata, datasets can contain cohort-level and resolution-level metadata, which are nested within the main metadata dictionary and apply only to subsets of images in the dataset.imaging(MacNicol et al, 2021),…”

mentioning

confidence: 99%

TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

Ćirić

Thompson

Lorenz

et al. 2021

Preprint

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RS2-Net: An end-to-end deep learning framework for rodent skull stripping in multi-center brain MRI

Lin,

Ding,

Chang

et al. 2024

NeuroImage

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Automatic brain extraction and brain tissues segmentation on multi-contrast animal MRI

Eddin¹,

Dorez²,

Curcio³

2023

Sci Rep

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For many neuroscience applications, brain extraction in MRI images is the first pre-processing step of a quantification pipeline. Once the brain is extracted, further post-processing calculations become faster, more specific and easier to implement and interpret. It is the case, for example, of functional MRI brain studies, or relaxation time mappings and brain tissues classifications to characterise brain pathologies. Existing brain extraction tools are mostly adapted to work on the human anatomy, this gives poor results when applied to animal brain images. We have developed an atlas-based Veterinary Images Brain Extraction (VIBE) algorithm that encompasses a pre-processing step to adapt the atlas to the patient’s image, and a subsequent registration step. We show that the brain extraction is achieved with excellent results in terms of Dice and Jaccard metrics. The algorithm is automatic, with no need to adapt the parameters in a broad range of situations: we successfully tested multiple MRI contrasts (T1-weighted, T2-weighted, T2-weighted FLAIR), all the acquisition planes (sagittal, dorsal, transverse), different animal species (dogs and cats) and canine cranial conformations (brachycephalic, mesocephalic, dolichocephalic). VIBE can be successfully extended to other animal species, provided that an atlas for that specific species exists. We show also how brain extraction, as a preliminary step, can help to segment brain tissues with a K-Means clustering algorithm.

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Atlas-Based Brain Extraction Is Robust Across RAT MRI Studies

Cited by 4 publications

References 17 publications

TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

RS2-Net: An end-to-end deep learning framework for rodent skull stripping in multi-center brain MRI

Automatic brain extraction and brain tissues segmentation on multi-contrast animal MRI

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