Combining imaging and shape features for prediction tasks of Alzheimer's disease classification and brain age regression

• URL: http://arxiv.org/abs/2501.07994v1
• Date: Tue, 14 Jan 2025 10:38:18 GMT
• Title: Combining imaging and shape features for prediction tasks of Alzheimer's disease classification and brain age regression
• Authors: Nairouz Shehata, Carolina Piçarra, Ben Glocker,
• Abstract summary: We investigate combining imaging and shape features extracted from MRI for the clinically relevant tasks of brain age prediction and Alzheimer's disease classification.<n>Our proposed model fuses ResNet-extracted image embeddings with shape embeddings from a bespoke graph neural network.<n>We observe improvements in the prediction performance on both tasks, with substantial gains for classification.
• Score: 14.07305121122096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate combining imaging and shape features extracted from MRI for the clinically relevant tasks of brain age prediction and Alzheimer's disease classification. Our proposed model fuses ResNet-extracted image embeddings with shape embeddings from a bespoke graph neural network. The shape embeddings are derived from surface meshes of 15 brain structures, capturing detailed geometric information. Combined with the appearance features from T1-weighted images, we observe improvements in the prediction performance on both tasks, with substantial gains for classification. We evaluate the model using public datasets, including CamCAN, IXI, and OASIS3, demonstrating the effectiveness of fusing imaging and shape features for brain analysis.

Related papers

• Segment Anything for Dendrites from Electron Microscopy [36.25707481854301]
  We present DendriteSAM, a vision foundation model based on Segment Anything, for interactive and automatic segmentation of dendrites in EM images. The model is trained on high-resolution EM data from healthy hippocampus rat and is tested on diseased rat and human data.
  arXiv  Detail & Related papers  (2024-11-04T19:54:26Z)
• Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation [53.70131202548981]
  We present a two-step segmentation framework employing Knowledge-Guided Prompt Learning (KGPL) for brain MRI. Specifically, we first pre-train segmentation models on large-scale datasets with sub-optimal labels. The introduction of knowledge-wise prompts captures semantic relationships between anatomical variability and biological processes.
  arXiv  Detail & Related papers  (2024-07-31T04:32:43Z)
• Psychometry: An Omnifit Model for Image Reconstruction from Human Brain Activity [60.983327742457995]
  Reconstructing the viewed images from human brain activity bridges human and computer vision through the Brain-Computer Interface. We devise Psychometry, an omnifit model for reconstructing images from functional Magnetic Resonance Imaging (fMRI) obtained from different subjects.
  arXiv  Detail & Related papers  (2024-03-29T07:16:34Z)
• Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction [41.018326648532586]
  Shape analysis in brain imaging can help interpret structural and functionality correlations of the human brain. We reconstruct brain connections as sequences of 3D points using diffusion magnetic resonance imaging (dMRI) tractography. We introduce a novel framework, Shape--fused Fiber Cluster Transformer (SFFormer), that leverages a multi-head cross-attention feature fusion module to predict subject-specific language performance.
  arXiv  Detail & Related papers  (2024-03-27T20:51:02Z)
• Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease [2.6793044027881865]
  This study proposes a novel Alzheimer's disease detection model based on Convolutional Neural Networks. The experimental results indicate that the employed 2D fusion algorithm effectively improves the model's training expense.
  arXiv  Detail & Related papers  (2024-01-25T12:18:46Z)
• Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
  We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces. We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain. We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
  arXiv  Detail & Related papers  (2024-01-05T19:45:12Z)
• Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results [1.2400966570867322]
  We present a novel technique to discover and exploit weak causal signals directly from images via neural networks for classification purposes. This way, we model how the presence of a feature in one part of the image affects the appearance of another feature in a different part of the image. Our method consists of a convolutional neural network backbone and a causality-factors extractor module, which computes weights to enhance each feature map according to its causal influence in the scene.
  arXiv  Detail & Related papers  (2023-09-19T08:00:26Z)
• Controllable Mind Visual Diffusion Model [58.83896307930354]
  Brain signal visualization has emerged as an active research area, serving as a critical interface between the human visual system and computer vision models. We propose a novel approach, referred to as Controllable Mind Visual Model Diffusion (CMVDM) CMVDM extracts semantic and silhouette information from fMRI data using attribute alignment and assistant networks. We then leverage a control model to fully exploit the extracted information for image synthesis, resulting in generated images that closely resemble the visual stimuli in terms of semantics and silhouette.
  arXiv  Detail & Related papers  (2023-05-17T11:36:40Z)
• 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)
• Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Data [8.388888908045406]
  We propose a potential solution by first learning a structural-to-functional transformation in brain MRI. We then synthesize spatially matched functional images from large-scale structural scans. We identify the temporal lobe to be the most predictive structural-region and the parieto-occipital lobe to be the most predictive functional-region of our model.
  arXiv  Detail & Related papers  (2021-04-10T03:16:33Z)
• Interpretation of 3D CNNs for Brain MRI Data Classification [56.895060189929055]
  We extend the previous findings in gender differences from diffusion-tensor imaging on T1 brain MRI scans. We provide the voxel-wise 3D CNN interpretation comparing the results of three interpretation methods.
  arXiv  Detail & Related papers  (2020-06-20T17:56:46Z)

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.