DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction
- URL: http://arxiv.org/abs/2010.11423v1
- Date: Thu, 22 Oct 2020 03:57:44 GMT
- Title: DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction
- Authors: Rodrigo Santa Cruz, Leo Lebrat, Pierrick Bourgeat, Clinton Fookes,
Jurgen Fripp, Olivier Salvado
- Abstract summary: DeepCSR is a 3D deep learning framework for cortical surface reconstruction from MRI.
It efficiently reconstructs cortical surfaces at high resolution capturing fine details in the cortical folding.
DeepCSR is as accurate, more precise, and faster than the widely used FreeSurfer toolbox and its deep learning powered variant FastSurfer.
- Score: 20.977071784183256
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The study of neurodegenerative diseases relies on the reconstruction and
analysis of the brain cortex from magnetic resonance imaging (MRI). Traditional
frameworks for this task like FreeSurfer demand lengthy runtimes, while its
accelerated variant FastSurfer still relies on a voxel-wise segmentation which
is limited by its resolution to capture narrow continuous objects as cortical
surfaces. Having these limitations in mind, we propose DeepCSR, a 3D deep
learning framework for cortical surface reconstruction from MRI. Towards this
end, we train a neural network model with hypercolumn features to predict
implicit surface representations for points in a brain template space. After
training, the cortical surface at a desired level of detail is obtained by
evaluating surface representations at specific coordinates, and subsequently
applying a topology correction algorithm and an isosurface extraction method.
Thanks to the continuous nature of this approach and the efficacy of its
hypercolumn features scheme, DeepCSR efficiently reconstructs cortical surfaces
at high resolution capturing fine details in the cortical folding. 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 facilitate large-scale
medical studies and new healthcare applications.
Related papers
- N-BVH: Neural ray queries with bounding volume hierarchies [51.430495562430565]
In 3D computer graphics, the bulk of a scene's memory usage is due to polygons and textures.
We devise N-BVH, a neural compression architecture designed to answer arbitrary ray queries in 3D.
Our method provides faithful approximations of visibility, depth, and appearance attributes.
arXiv Detail & Related papers (2024-05-25T13:54:34Z) - Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape
Reconstruction from Point Clouds [53.02191521770926]
We introduce Neural Poisson Surface Reconstruction (nPSR), an architecture for shape reconstruction that addresses the challenge of recovering 3D shapes from points.
nPSR exhibits two main advantages: First, it enables efficient training on low-resolution data while achieving comparable performance at high-resolution evaluation.
Overall, the neural Poisson surface reconstruction not only improves upon the limitations of classical deep neural networks in shape reconstruction but also achieves superior results in terms of reconstruction quality, running time, and resolution agnosticism.
arXiv Detail & Related papers (2023-08-03T13:56:07Z) - Joint Reconstruction and Parcellation of Cortical Surfaces [3.9198548406564604]
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 disease (AD)
In this work, we propose two options, one based on a graph classification branch and another based on a novel generic 3D reconstruction loss, to augment template-deformation algorithms.
We attain highly accurate parcellations with a Dice score of 90.2 (graph classification branch) and 90.4 (novel reconstruction loss) together with state-of-the-art surfaces.
arXiv Detail & Related papers (2022-09-19T11:45:39Z) - MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface
Reconstruction [72.05649682685197]
State-of-the-art neural implicit methods allow for high-quality reconstructions of simple scenes from many input views.
This is caused primarily by the inherent ambiguity in the RGB reconstruction loss that does not provide enough constraints.
Motivated by recent advances in the area of monocular geometry prediction, we explore the utility these cues provide for improving neural implicit surface reconstruction.
arXiv Detail & Related papers (2022-06-01T17:58:15Z) - Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D
MRI Scans with Geometric Deep Neural Networks [3.364554138758565]
We propose Vox2Cortex, a deep learning-based algorithm that directly yields topologically correct, three-dimensional meshes of the boundaries of the cortex.
We show in extensive experiments on three brain MRI datasets that our meshes are as accurate as the ones reconstructed by state-of-the-art methods in the field.
arXiv Detail & Related papers (2022-03-17T17:06:00Z) - NeuS: Learning Neural Implicit Surfaces by Volume Rendering for
Multi-view Reconstruction [88.02850205432763]
We present a novel neural surface reconstruction method, called NeuS, for reconstructing objects and scenes with high fidelity from 2D image inputs.
Existing neural surface reconstruction approaches, such as DVR and IDR, require foreground mask as supervision.
We observe that the conventional volume rendering method causes inherent geometric errors for surface reconstruction.
We propose a new formulation that is free of bias in the first order of approximation, thus leading to more accurate surface reconstruction even without the mask supervision.
arXiv Detail & Related papers (2021-06-20T12:59:42Z) - A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction [1.8047694351309207]
We propose a novel deep-learning-based approach that directly predicts whole heart surface meshes from volumetric CT and MR image data.
Our method demonstrated promising performance of generating high-resolution and high-quality whole heart reconstructions.
arXiv Detail & Related papers (2021-02-16T00:39:43Z) - Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction.
Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
arXiv Detail & Related papers (2020-10-05T14:18:52Z) - Surface Agnostic Metrics for Cortical Volume Segmentation and Regression [3.1543820811374483]
We propose a machine learning solution to predict cortical thickness and curvature metrics from T2 MRI images.
Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies.
arXiv Detail & Related papers (2020-10-04T19:46:04Z) - Deep Modeling of Growth Trajectories for Longitudinal Prediction of
Missing Infant Cortical Surfaces [58.780482825156035]
We will introduce a method for longitudinal prediction of cortical surfaces using a spatial graph convolutional neural network (GCNN)
The proposed method is designed to model the cortical growth trajectories and jointly predict inner and outer curved surfaces at multiple time points.
We will demonstrate with experimental results that our method is capable of capturing the nonlinearity oftemporal cortical growth patterns.
arXiv Detail & Related papers (2020-09-06T18:46:04Z) - A Two-step Surface-based 3D Deep Learning Pipeline for Segmentation of
Intracranial Aneurysms [18.163031102785904]
We offer a two-step surface-based deep learning pipeline that achieves significantly higher performance.
A user first generates a surface model by manually specifying multiple thresholds for time-of-flight magnetic resonance angiography images.
The system then samples small surface fragments from the entire brain arteries and classifies the surface fragments according to whether aneurysms are present.
arXiv Detail & Related papers (2020-06-29T16:23:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.