Surface Agnostic Metrics for Cortical Volume Segmentation and Regression

• URL: http://arxiv.org/abs/2010.01669v1
• Date: Sun, 4 Oct 2020 19:46:04 GMT
• Title: Surface Agnostic Metrics for Cortical Volume Segmentation and Regression
• Authors: Samuel Budd, Prachi Patkee, Ana Baburamani, Mary Rutherford, Emma C. Robinson, Bernhard Kainz
• Abstract summary: 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.
• Score: 3.1543820811374483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current analysis of cortical variation is typically performed by fitting surface mesh models to inner and outer cortical boundaries and investigating metrics such as surface area and cortical curvature or thickness. These, however, take a long time to run, and are sensitive to motion and image and surface resolution, which can prohibit their use in clinical settings. In this paper, we instead propose a machine learning solution, training a novel architecture to predict cortical thickness and curvature metrics from T2 MRI images, while additionally returning metrics of prediction uncertainty. Our proposed model is tested on a clinical cohort (Down Syndrome) for which surface-based modelling often fails. Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies.

Related papers

• Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators [72.79532467687427]
  Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled and compressed measurements. Deep neural networks have shown great potential for reconstructing high-quality images from highly undersampled measurements. We propose a unified model that is robust to different subsampling patterns and image resolutions in CS-MRI.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Neural deformation fields for template-based reconstruction of cortical surfaces from MRI [5.4173776411667935]
  We introduce Vox2Cortex-Flow, a deep mesh-deformation technique that learns a deformation field from a brain template to the cortical surfaces of an MRI scan. V2C-Flow is not only very fast, requiring less than two seconds to infer all four cortical surfaces. We show that V2C-Flow results in cortical surfaces that are state-of-the-art in terms of accuracy.
  arXiv  Detail & Related papers  (2024-01-23T17:50:58Z)
• Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studies [2.930354460501359]
  Surface analysis of the cortex is ubiquitous in human neuroimaging with MRI, e.g., for cortical registration, parcellation, or thickness estimation. Here we present the first method for cortical reconstruction, registration, parcellation, and thickness estimation for clinical brain MRI scans of any resolution and pulse sequence.
  arXiv  Detail & Related papers  (2023-05-02T23:36:06Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• 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)
• Brain Cortical Functional Gradients Predict Cortical Folding Patterns via Attention Mesh Convolution [51.333918985340425]
  We develop a novel attention mesh convolution model to predict cortical gyro-sulcal segmentation maps on individual brains. Experiments show that the prediction performance via our model outperforms other state-of-the-art models.
  arXiv  Detail & Related papers  (2022-05-21T14:08:53Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• Predicting Future Cognitive Decline with Hyperbolic Stochastic Coding [0.7637291629898925]
  We propose a novel framework termed as hyperbolic coding (HSC) Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface based brain imaging research tools and potentially in a diagnostic and prognostic indicator to be useful in individualized treatment strategies.
  arXiv  Detail & Related papers  (2021-02-21T04:07:27Z)
• 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)
• 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)

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.