Joint cortical registration of geometry and function using semi-supervised learning

• URL: http://arxiv.org/abs/2303.01592v4
• Date: Mon, 16 Oct 2023 21:30:14 GMT
• Title: Joint cortical registration of geometry and function using semi-supervised learning
• Authors: Jian Li, Greta Tuckute, Evelina Fedorenko, Brian L. Edlow, Bruce Fischl, Adrian V. Dalca
• Abstract summary: We introduce a learning-based cortical registration framework, JOSA, which jointly aligns folding patterns and functional maps while simultaneously learning an optimal atlas. We demonstrate that JOSA can substantially improve registration performance in both anatomical and functional domains over existing methods.
• Score: 10.584603337042534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learning-based approaches focus on accurate registration of folding patterns of the cerebral cortex, and assume that geometry predicts function and thus functional areas will also be well aligned. However, structure/functional variability of anatomically corresponding areas across subjects has been widely reported. In this work, we introduce a learning-based cortical registration framework, JOSA, which jointly aligns folding patterns and functional maps while simultaneously learning an optimal atlas. We demonstrate that JOSA can substantially improve registration performance in both anatomical and functional domains over existing methods. By employing a semi-supervised training strategy, the proposed framework obviates the need for functional data during inference, enabling its use in broad neuroscientific domains where functional data may not be observed. The source code of JOSA will be released to the public at https://voxelmorph.net.

Related papers

• Unified Brain Surface and Volume Registration [27.427968036901678]
  NeurAlign registers $3$D brain MRI images by jointly aligning both cortical and subcortical regions.<n>Our method consistently outperforms both classical and machine learning-based registration methods.<n>With its superior accuracy, fast inference, and ease of use, NeurAlign sets a new standard for joint cortical and subcortical registration.
  arXiv  Detail & Related papers  (2025-12-22T23:05:26Z)
• Deformable Attention Graph Representation Learning for Histopathology Whole Slide Image Analysis [9.724220291296927]
  We propose a novel GNN framework with deformable attention for pathology image analysis.<n>We construct a dynamic weighted directed graph based on patch features, where each node aggregates contextual information from its neighbors via attention-weighted edges.<n>Specifically, we incorporate learnable spatial offsets informed by the real coordinates of each patch, enabling the model to adaptively attend to morphologically relevant regions across the slide.
  arXiv  Detail & Related papers  (2025-08-07T13:30:29Z)
• GESH-Net: Graph-Enhanced Spherical Harmonic Convolutional Networks for Cortical Surface Registration [8.896542371748115]
  This paper constructs a deep learning model to study the technology of cortical surface image registration. An unsupervised cortical surface registration network based on a multi-scale cascaded structure is designed. A graph-enhenced module is introduced into the registration network, using the graph attention module to help the network learn global features.
  arXiv  Detail & Related papers  (2024-10-18T18:21:47Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• JOSA: Joint surface-based registration and atlas construction of brain geometry and function [10.584603337042532]
  JOSA is a novel cortical registration framework that jointly models the mismatch between geometry and function. It achieves superior registration performance in both geometry and function to the state-of-the-art methods but without requiring functional data at inference. It provides new insights into the future development of registration methods using joint analysis of the brain structure and function.
  arXiv  Detail & Related papers  (2023-10-22T02:16:48Z)
• Automatic Landmark Detection and Registration of Brain Cortical Surfaces via Quasi-Conformal Geometry and Convolutional Neural Networks [17.78250777571423]
  We propose a novel framework for the automatic landmark detection and registration of brain cortical surfaces. We first develop a landmark detection network (LD-Net) that allows for the automatic extraction of landmark curves. We then utilize the detected landmarks and quasi-conformal theory for achieving the surface registration.
  arXiv  Detail & Related papers  (2022-08-15T05:47:51Z)
• 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)
• Functional2Structural: Cross-Modality Brain Networks Representation Learning [55.24969686433101]
  Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
  arXiv  Detail & Related papers  (2022-05-06T03:45:36Z)
• An explainability framework for cortical surface-based deep learning [110.83289076967895]
  We develop a framework for cortical surface-based deep learning. First, we adapted a perturbation-based approach for use with surface data. We show that our explainability framework is not only able to identify important features and their spatial location but that it is also reliable and valid.
  arXiv  Detail & Related papers  (2022-03-15T23:16:49Z)
• Kinship Verification Based on Cross-Generation Feature Interaction Learning [53.62256887837659]
  Kinship verification from facial images has been recognized as an emerging yet challenging technique in computer vision applications. We propose a novel cross-generation feature interaction learning (CFIL) framework for robust kinship verification.
  arXiv  Detail & Related papers  (2021-09-07T01:50:50Z)
• Transforming Feature Space to Interpret Machine Learning Models [91.62936410696409]
  This contribution proposes a novel approach that interprets machine-learning models through the lens of feature space transformations. It can be used to enhance unconditional as well as conditional post-hoc diagnostic tools. A case study on remote-sensing landcover classification with 46 features is used to demonstrate the potential of the proposed approach.
  arXiv  Detail & Related papers  (2021-04-09T10:48:11Z)

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.