A Deep Learning-Based Tool for Automatic Brain Extraction from Functional Magnetic Resonance Images of Rodents

Pontes-Filho, Sidney; Dahl, Annelene Gulden; Nichele, Stefano; Mello, Gustavo Borges Moreno e

doi:10.1007/978-3-030-82199-9_36

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Unfortunately, most methods for skull‐stripping developed for human fail both challenges, as these methods use intensity patterns to identify boundaries between brain and skull or other segmentation‐type strategies and are based on the more spherical human brain shape 275–277,279 . Other programs applicable to rodents similarly use intensity‐based segmentations, including AFNI 3dSkullStrip, 267 three‐dimensional pulse‐coupled neural networks (3D‐PCNN), 280 Rodent Brain Extraction Tool (RBET), 281 Rapid Automatic Tissue Segmentation (RATS), 282 SHape descriptor selected Extremal Regions after Morphologically filtering (SHERM), 283 and deep learning programs that use a U‐Net architecture 284,285 . One such U‐Net skull‐stripping program was developed specifically to address distortions unique to BOLD imaging that occur in the setting of lower spatial resolution and susceptibility‐induced distortions that may occur during BOLD imaging 285 .…”

Section: Statistical and Computational Analysesmentioning

confidence: 99%

“…Other programs applicable to rodents similarly use intensity‐based segmentations, including AFNI 3dSkullStrip, 267 three‐dimensional pulse‐coupled neural networks (3D‐PCNN), 280 Rodent Brain Extraction Tool (RBET), 281 Rapid Automatic Tissue Segmentation (RATS), 282 SHape descriptor selected Extremal Regions after Morphologically filtering (SHERM), 283 and deep learning programs that use a U‐Net architecture 284,285 . One such U‐Net skull‐stripping program was developed specifically to address distortions unique to BOLD imaging that occur in the setting of lower spatial resolution and susceptibility‐induced distortions that may occur during BOLD imaging 285 . Brain extraction procedures are further reviewed by Feo and Giove 286,287 …”

Section: Statistical and Computational Analysesmentioning

confidence: 99%

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Longitudinal manganese‐enhanced magnetic resonance imaging of neural projections and activity

et al. 2022

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Manganese-enhanced magnetic resonance imaging (MEMRI) holds exceptional promise for preclinical studies of brain-wide physiology in awake-behaving animals. The objectives of this review are to update the current information regarding MEMRI and to inform new investigators as to its potential. Mn(II) is a powerful contrast agent for two main reasons: (1) high signal intensity at low doses; and (2) biological interactions, such as projection tracing and neural activity mapping via entry into electrically active neurons in the living brain. High-spin Mn(II) reduces the relaxation time of water protons: at Mn(II) concentrations typically encountered in MEMRI, robust hyperintensity is obtained without adverse effects. By selectively entering neurons through voltagegated calcium channels, Mn(II) highlights active neurons. Safe doses may be repeated over weeks to allow for longitudinal imaging of brain-wide dynamics in the same individual across time. When delivered by stereotactic intracerebral injection, Mn(II) enters active neurons at the injection site and then travels inside axons for long distances, tracing neuronal projection anatomy. Rates of axonal transport within the brain were measured for the first time in "time-lapse" MEMRI. When delivered systemically, Mn(II) enters active neurons throughout the brain via voltage-sensitive calcium channels and clears slowly. Thus behavior can be monitored during Mn(II) uptake and hyperintense signals due to Mn(II) uptake captured retrospectively, allowing pairing of behavior with neural activity maps for the first time. Here we review critical information gained from MEMRI projection mapping about human

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Section: Statistical and Computational Analysesmentioning

confidence: 99%

Section: Statistical and Computational Analysesmentioning

confidence: 99%

Longitudinal manganese‐enhanced magnetic resonance imaging of neural projections and activity

et al. 2022

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“…Traditional brain extraction tools for rodent MRI have demonstrated successful automation with high accuracy [21] [6] [17] [24]; however, there remain practical limitations due a need for parameter optimization based on specific sequences used and a high failure rate when applying tools on new datasets, particularly due to pathology in preclinical trials involving brain trauma. Stateof-the-art approaches for brain extraction in non-human models now use convolutional neural networks (CNNs) [15] [9] [22] [26], which employ the U-Net architecture developed by Ronneberger et al [23], which has shown to provide superior performance across a wide range of biomedical segmentation tasks [16]. Nearly all previous work has shown that U-Net used on rodent models significantly improves the accuracy and reduces the time of rodent brain extract.…”

Section: Introductionmentioning

confidence: 99%

Evaluating U-net Brain Extraction for Multi-site and Longitudinal Preclinical Stroke Imaging

Tarakci¹,

Mandeville²,

Hyder³

et al. 2022

Preprint

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Rodent stroke models are important for evaluating treatments and understanding the pathophysiology and behavioral changes of brain ischemia, and magnetic resonance imaging (MRI) is a valuable tool for measuring outcome in preclinical studies. Brain extraction is an essential first step in most neuroimaging pipelines; however, it can be challenging in the presence of severe pathology and when dataset quality is highly variable. Convolutional neural networks (CNNs) can improve accuracy and reduce operator time, facilitating high throughput preclinical studies. As part of an ongoing preclinical stroke imaging study, we developed a deep-learning mouse brain extraction tool by using a Unet CNN. While previous studies have evaluated U-net architectures, we sought to evaluate their practical performance across data types. We ask how performance is affected with data across: six imaging centers, two time points after experimental stroke, and across four MRI contrasts. We trained, validated, and tested a typical U-net model on 240 multimodal MRI datasets including quantitative multi-echo T2 and apparent diffusivity coefficient (ADC) maps, and performed qualitative evaluation with a large preclinical stroke database (N=1,368). We describe the

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Fully automated whole brain segmentation from rat MRI scans with a convolutional neural network

Porter,

Hobson,

Foster

et al. 2024

Journal of Neuroscience Methods

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A Deep Learning-Based Tool for Automatic Brain Extraction from Functional Magnetic Resonance Images of Rodents

Cited by 4 publications

References 19 publications

Longitudinal manganese‐enhanced magnetic resonance imaging of neural projections and activity

Longitudinal manganese‐enhanced magnetic resonance imaging of neural projections and activity

Evaluating U-net Brain Extraction for Multi-site and Longitudinal Preclinical Stroke Imaging

Fully automated whole brain segmentation from rat MRI scans with a convolutional neural network

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