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3D whole brain segmentation using spatially localized atlas network tiles
2019
NeuroImage
Preserved Fulltext
In this work, we propose the spatially localized atlas network tiles (SLANT) method to distribute multiple independent 3D fully convolutional networks (FCN) for high-resolution whole brain segmentation ...
Recently, deep convolution neural network (CNN) has been applied to whole brain segmentation. ...
This study was in part using the resources of the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University, Nashville, TN. ...
doi:10.1016/j.neuroimage.2019.03.041
pmid:30910724
pmcid:PMC6536356
fatcat:3uq5pbxd4jcntguwpujlneyfgu
Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
[article]
2018
arXiv
pre-print
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In this paper, we propose the spatially localized atlas network tiles (SLANT) method to distribute multiple independent 3D fully convolutional networks to cover overlapped sub-spaces in a standard atlas ...
Yet, few previous efforts have been made on detailed whole brain segmentation using 3D networks due to the following challenges: (1) fitting entire whole brain volume into 3D networks is restricted by ...
In this paper, we propose the spatially localized atlas network tiles (SLANT) method for detailed whole brain segmentation (133 labels under BrainCOLOR protocol [6] ) by combining canonical medical image ...
arXiv:1806.00546v2
fatcat:ptpsn7qn35gujh4pc6xo2gmrma
Improving segmentation reliability of multi-scanner brain images using a generative adversarial network
2021
Quantitative Imaging in Medicine and Surgery
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The spatially localized atlas network tiles-27 (SLANT-27) deep learning model was used to train the automatic segmentation module, based on a multi-center dataset of 1,917 three-dimensional (3D) T1-weighted ...
The newly developed QBrain method combined with GAN image transfer module and a SLANT-27 segmentation module was shown to improve the reliability of whole-brain automatic structural segmentation results ...
Neuronet: fast and robust reproduction of
whole brain segmentation using spatially localized atlas multiple brain image segmentation pipelines. arXiv
network tiles. ...
doi:10.21037/qims-21-653
pmid:35284270
pmcid:PMC8899955
fatcat:rio22psyj5ghlare6a6nlpujue
Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols
[article]
2019
arXiv
pre-print
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Recently, we proposed the spatially localized atlas network tiles (SLANT) method, which is able to segment a 3D MRI brain scan into 132 anatomical regions. ...
Traditionally, multi-atlas segmentation has been regarded as the standard method for whole brain segmentation. ...
accuracy compared with MAS such as the recently proposed spatially localized atlas network tiles (SLANT) method [9] (https://github.com/MASILab/SLANT_brain_seg). ...
arXiv:1901.02040v1
fatcat:g2ievxgy3zgaxd6dogaqadfbkm
MarmoNet: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography
[article]
2019
arXiv
pre-print
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In the Brain/MINDS project, a connectivity study on marmoset brains uses two-photon microscopy fluorescence images of axonal projections to collect the neuron connectivity from defined brain regions at ...
The processing of the images requires the detection and segmentation of the axonal tracer signal. ...
The atlas is in alignment with a brain reference image space based on a T2 weighted MRI population average brain image. The spatial resolution of the T2 weighted MRI image is 100 3 µm 3 . ...
arXiv:1908.00876v1
fatcat:tojkvfyxkrgjfk3q4jgvfkbvdq
Whole-brain mapping of behaviourally induced neural activation in mice
2014
Brain Structure and Function
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processing algorithms to count the activated neurons and align the datasets to the Allen Mouse Brain Atlas; and statistical analysis to identify the network of activated brain regions evoked by behaviour ...
However, current imaging methods are limited in their spatial resolution and/or ability to obtain brain-wide coverage of functional activity. ...
Understanding these networks therefore requires whole-brain approaches. ...
doi:10.1007/s00429-014-0774-0
pmid:24760545
fatcat:ugc47xdwqremfirnmvxg2geo4i
CEREBRUM-7T: fast and fully-volumetric brain segmentation of out-of-the-scanner 7T MR volumes
[article]
2020
bioRxiv
pre-print
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In this work, we design and test CEREBRUM-7T, an optimised end-to-end CNN architecture, that allows to segment a whole 7T T1w MRI brain volume at once, without the need of partitioning it into 2D or 3D ...
tiles. ...
Lucy Petro for comments improving the manuscript, and Mrs Frances Crabbe for useful feedbacks on manual segmentation.
Author Contributions ...
doi:10.1101/2020.07.07.191536
fatcat:jywisqltfbhxnfgkbd5z5djxte
Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas
2011
Frontiers in Neuroinformatics
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KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). ...
Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown ...
DATABASES OF 3D RECONSTRUCTION OF NEURONS The low spatial resolution in existing whole-brain level brain maps and atlases have been pointed out as a major limitation. ...
doi:10.3389/fninf.2011.00029
pmid:22275895
pmcid:PMC3254184
fatcat:4amvlcja2fee3iavoukmihfo24
Automated analysis of whole brain vasculature using machine learning
[article]
2019
bioRxiv
pre-print
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Our pipeline uses a fully convolutional network with a transfer learning approach for segmentation. ...
We systematically analyzed vascular features of the whole brains including their length, bifurcation points and radius at the micrometer scale by registering them to the Allen mouse brain atlas. ...
However, such methods including local spatial regularization cannot segment large vascular networks across changing intensity distributions. ...
doi:10.1101/613257
fatcat:233qaac4mbf53h42x2vlrbs74u
Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
2020
Applied Sciences
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brain. ...
In this paper, the current methods of skull stripping have been divided into two distinct groups—conventional or classical approaches, and convolutional neural networks or deep learning approaches. ...
To solve those two problems, the author proposed the spatially localized atlas network tiles (SLANT) method. Multiple spatially distributed networks were used to overcome the spatial limitation. ...
doi:10.3390/app10051773
fatcat:nwp2z2y4jzgoxinv7sfppbfrfa
Whole brain segmentation with full volume neural network
2021
Computerized Medical Imaging and Graphics
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2110.15601v1 fatcat:ombc2qo6q5djzb4ep74xn5gv6m
Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest. ...
To address these issues, we propose to adopt a full volume framework, which feeds the full volume brain image into the segmentation network and directly outputs the segmentation result for the whole brain ...
[2] developed a spatially localized atlas network tiles (SLANT) method, which is subvolume-based, where multiple spatially location-specific FCNs are trained in parallel, with auxiliary labels generated ...
doi:10.1016/j.compmedimag.2021.101991
pmid:34634548
fatcat:c4wq6dnmxvfyllgmkra7j3dg3i
Split-Attention U-Net: A Fully Convolutional Network for Robust Multi-Label Segmentation from Brain MRI
2020
Brain Sciences
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Therefore, we introduce Split-Attention U-Net (SAU-Net), a convolutional neural network with skip pathways and a split-attention module that segments brain MRI scans. ...
Multi-label brain segmentation from brain magnetic resonance imaging (MRI) provides valuable structural information for most neurological analyses. ...
Brain Sci. 2020, 10, 974 ...
doi:10.3390/brainsci10120974
pmid:33322640
pmcid:PMC7764312
fatcat:rge2dyuogvggfbcyxaxb37o4ra
AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation
[article]
2019
arXiv
pre-print
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Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. ...
To address this problem, previous DL methods proposed to use a single convolution neural network (CNN) or few independent CNNs. ...
The C-MIND data used in the preparation of this article were obtained from the C-MIND Data Repository (accessed in Feb 2015) created by the C-MIND study of Normal Brain Development. ...
arXiv:1911.09098v1
fatcat:yq5blqsxbzcenlikojkhphq5oq
ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation
[article]
2021
arXiv
pre-print
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In order to overcome this limitation, we develop an Anatomical Context-Encoding Network (ACEnet) to incorporate 3D spatial and anatomical contexts in 2D convolutional neural networks (CNNs) for efficient ...
However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation. ...
to segment the brain structures using spatially localized atlas network tiles (SLANT) (Huo et al., 2019) ; and a transfer learning method was developed to segment the brain structures by learning from ...
arXiv:2002.05773v3
fatcat:7xqhiedtgjbjhkiku3ge6zpava
A mesoscale connectome of the mouse brain
2014
Nature
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This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. ...
At the mesoscale, both long-range and local connections can be described using a sampling approach with various neuroanatomical tracers that enable whole-brain mapping in a reasonable time frame across ...
Second, the average template brain was aligned with the 3D reference model, again using local alignment (Supplementary Video 2). ...
doi:10.1038/nature13186
pmid:24695228
pmcid:PMC5102064
fatcat:laxll423ajgabo2atmscv5inx4
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