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Fast cortical surface reconstruction from MRI using deep learning
2022
Brain Informatics
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To address this challenge, we propose a fast cortical surface reconstruction (FastCSR) pipeline by leveraging deep machine learning. ...
AbstractReconstructing cortical surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based functional and anatomical image analyses. ...
Here, we propose a novel, fast cortical surface reconstruction (FastCSR) algorithm based on deep learning. ...
doi:10.1186/s40708-022-00155-7
pmid:35262808
pmcid:PMC8907118
fatcat:z45geoz5crefrowd26oiyi55o4
PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction
[article]
2021
arXiv
pre-print
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Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). ...
In this work, we introduce Pial Neural Network (PialNN), a 3D deep learning framework for pial surface reconstruction. ...
Conclusion PialNN is a fast and memory-efficient deep learning framework for cortical pial surface reconstruction. ...
arXiv:2109.03693v1
fatcat:q4sbwk724ngrlortqmmhkmvjbi
DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction
[article]
2020
arXiv
pre-print
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Having these limitations in mind, we propose DeepCSR, a 3D deep learning framework for cortical surface reconstruction from MRI. ...
Moreover, DeepCSR is as accurate, more precise, and faster than the widely used FreeSurfer toolbox and its deep learning powered variant FastSurfer on reconstructing cortical surfaces from MRI which should ...
In this paper, we propose a 3D deep learning framework for cortical surface reconstruction from MR images named DeepCSR. ...
arXiv:2010.11423v1
fatcat:yuiaqrjx75gexcl3vhghblhfi4
SurfNN: Joint Reconstruction of Multiple Cortical Surfaces from Magnetic Resonance Images
[article]
2023
arXiv
pre-print
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To achieve fast, robust, and accurate reconstruction of the human cortical surfaces from 3D magnetic resonance images (MRIs), we develop a novel deep learning-based framework, referred to as SurfNN, to ...
Different from existing deep learning-based cortical surface reconstruction methods that either reconstruct the cortical surfaces separately or neglect the interdependence between the inner and outer surfaces ...
COMPLIANCE WITH ETHICAL STANDARDS This study was conducted retrospectively using public data [13] .
ACKNOWLEDGMENTS This work was supported in part by the NIH grant AG066650 and EB022573. ...
arXiv:2303.02922v1
fatcat:snzi4fyvtbfupklofhhkxncofe
CorticalFlow^++: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability
[article]
2022
arXiv
pre-print
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Recently, supervised deep learning approaches have been introduced to speed up this task cutting down the reconstruction time from hours to seconds. ...
The problem of Cortical Surface Reconstruction from magnetic resonance imaging has been traditionally addressed using lengthy pipelines of image processing techniques like FreeSurfer, CAT, or CIVET. ...
target cortical surface from an input MRI. ...
arXiv:2206.06598v1
fatcat:2lkklx27uza2rcmien5yhlfp4y
CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs
[article]
2022
arXiv
pre-print
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We present CortexODE, a deep learning framework for cortical surface reconstruction. ...
CortexODE can be integrated to an automatic learning-based pipeline, which reconstructs cortical surfaces efficiently in less than 5 seconds. ...
Learning-based Cortical Surface Reconstruction As an end-to-end alternative to traditional pipelines, deep learning approaches have shown great potential for the reconstruction of 3D shapes while exhibiting ...
arXiv:2202.08329v2
fatcat:lgmvezhb4ncopobpvmvykdjpz4
Joint Reconstruction and Parcellation of Cortical Surfaces
[article]
2022
arXiv
pre-print
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For the former task, powerful deep learning approaches, which provide highly accurate brain surfaces of tissue boundaries from input MRI scans in seconds, have recently been proposed. ...
The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative diseases like Alzheimer's ...
To avoid the lengthy runtime of FreeSurfer for surface generation, deep learning-based surface reconstruction approaches focus on the fast and accurate generation of cortical surfaces from MRI. ...
arXiv:2210.01772v1
fatcat:r33tnfysizay7j5xq3qlixhspe
Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks
[article]
2022
arXiv
pre-print
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The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology. ...
such as mesh extraction and topology correction (deep learning-based). ...
Existing deep learning-based approaches for cortical surface reconstruction from MRI usually rely on an implicit or voxel representation of the cortex. ...
arXiv:2203.09446v2
fatcat:h5xfyzberneldhhxgfyw5ii7am
A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI
[article]
2021
arXiv
pre-print
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We achieved average Dice similarity coefficient of 0.87, average Hausdorff distance of 0.96 mm, and average symmetric surface difference of 0.28 mm on reconstructed fetal brain MRI scans of fetuses scanned ...
Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical ...
Index Terms-Cortical plate, Automatic segmentation, Fetal MRI, Deep learning, Convolutional neural network, Attention
I. ...
arXiv:2004.12847v3
fatcat:r26i6yn4tnhhrn66ld2kfnrp6m
A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
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Deep Learning 460 DeepHCS: Bright-field to Fluorescence Microscopy Image Conversion using Deep Learning for Labelfree High-Content Screening 462 Volumetric Clipping Surface: Un-occluded visualization ...
from 2DUS via Deep Conditional Generative Networks 742 Simultaneous Surgical Visibility Assessment, Restoration, and Augmented Stereo Surface Reconstruction for Robotic Prostatectomy 743 Towards Efficient ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
FastSurfer – A fast and accurate deep learning based neuroimaging pipeline
[article]
2020
arXiv
pre-print
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Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. ...
including surface reconstruction and cortical parcellation. ...
Starting from the accurate 3D whole brain segmentation, provided by our deep learning framework, we perform cortical surface reconstruction and fast spherical mapping via a novel spectral approach that ...
arXiv:1910.03866v4
fatcat:qdbu5o3oqvf6jahzjfvtkrojgi
Computational neuroanatomy of baby brains: A review
2018
NeuroImage
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We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. ...
In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal ...
First, due to the severe partial volume effects in infant MRI, accurate reconstruction of the outer cortical surface in deep narrow sulcal regions for infant brains is more challenging than for adult brains ...
doi:10.1016/j.neuroimage.2018.03.042
pmid:29574033
pmcid:PMC6150852
fatcat:w2o27wt5afhktd7zoophmhnmi4
Segmentation of the cortical plate in fetal brain MRI with a topological loss
[article]
2020
arXiv
pre-print
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Furthermore, qualitative evaluation by three different experts on 130 randomly selected slices from 26 clinical MRIs evidences the out-performance of our method independently of the MR reconstruction quality ...
The fetal cortical plate undergoes drastic morphological changes throughout early in utero development that can be observed using magnetic resonance (MR) imaging. ...
More recently, deep learning methods have also focused on fetal brain MRI cortical gray matter segmentation. ...
arXiv:2010.12391v2
fatcat:dzyh5z6robblxlleo3e2flks6i
DeepPrep: An accelerated, scalable, and robust pipeline for neuroimaging preprocessing empowered by deep learning
[article]
2024
bioRxiv
pre-print
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Here, we present DeepPrep, a pipeline empowered by deep learning and workflow manager. ...
This process utilizes FastCSR 13 , a deep-learning-based model designed to accelerate cortical surface reconstruction. ...
Cortical surface reconstruction. The white-matter and pial cortical surfaces are reconstructed based on the anatomical segmentation derived from the FastSurferCNN 11 . ...
doi:10.1101/2024.03.06.581108
fatcat:rzu6fifhqvhijdorsd3yeizuoe
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline
2020
NeuroImage
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Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. ...
including surface reconstruction and cortical parcellation. ...
Further, data used in the preparation of this article were obtained from the MIRIAD database. The MIRIAD investigators did not participate in analysis or writing of this report. ...
doi:10.1016/j.neuroimage.2020.117012
pmid:32526386
pmcid:PMC7898243
fatcat:slpzns3hnnaf7g6arurhcjicy4
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