Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI

• URL: http://arxiv.org/abs/2205.03210v1
• Date: Thu, 5 May 2022 17:21:47 GMT
• Title: Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI
• Authors: Davood Karimi and Ali Gholipour
• Abstract summary: We propose the first framework to exploit both deep learning and atlases for biomarker estimation in dMRI. Our framework relies on non-linear diffusion registration tensor to compute biomarker atlases and to estimate atlas reliability maps. We use our framework to estimate an fractionalisotropy and neurite orientation dispersion from down-sampled dMRI data on a test cohort of 70 newborn subjects.
• Score: 8.219843232619551
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In this paper, we propose the first framework to exploit both deep learning and atlases for biomarker estimation in dMRI. Our framework relies on non-linear diffusion tensor registration to compute biomarker atlases and to estimate atlas reliability maps. We also use nonlinear tensor registration to align the atlas to a subject and to estimate the error of this alignment. We use the biomarker atlas, atlas reliability map, and alignment error map, in addition to the dMRI signal, as inputs to a deep learning model for biomarker estimation. We use our framework to estimate fractional anisotropy and neurite orientation dispersion from down-sampled dMRI data on a test cohort of 70 newborn subjects. Results show that our method significantly outperforms standard estimation methods as well as recent deep learning techniques. Our method is also more robust to stronger measurement down-sampling factors. Our study shows that the advantages of deep learning and atlases can be synergistically combined to achieve unprecedented accuracy in biomarker estimation from dMRI data.

Related papers

• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis [45.074243735766]
  We propose a physics-guided diffusion model to generate high-quality dMRI. Our model introduces the physical principles of dMRI in the noise evolution in the diffusion process. Our experiment results show that our method outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-05T07:09:19Z)
• Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation [47.250147322130545]
  Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. We present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.
  arXiv  Detail & Related papers  (2023-11-18T14:37:53Z)
• An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease [4.304406827494684]
  We propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features.
  arXiv  Detail & Related papers  (2023-08-15T13:54:50Z)
• Robust Fiber ODF Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI [7.9283612449524155]
  A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF) measurement variabilities (e.g., inter- and intra-site variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods (e.g., constrained spherical deconvolution (CSD)) and learning based methods (e.g., deep learning (DL)) do not explicitly consider such variabilities in fODF modeling. We propose a novel data-driven deep constrained spherical deconvolution method to
  arXiv  Detail & Related papers  (2023-06-05T14:06:40Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Prostate Lesion Detection and Salient Feature Assessment Using Zone-Based Classifiers [0.0]
  Multi-parametric magnetic resonance imaging (mpMRI) has a growing role in detecting prostate cancer lesions. It is pertinent that medical professionals who interpret these scans reduce the risk of human error by using computer-aided detection systems. Here we investigate the best machine learning classifier for each prostate zone.
  arXiv  Detail & Related papers  (2022-08-24T13:08:56Z)
• DeepTechnome: Mitigating Unknown Bias in Deep Learning Based Assessment of CT Images [44.62475518267084]
  We debias deep learning models during training against unknown bias. We use control regions as surrogates that carry information regarding the bias. Applying the proposed method to learn from data exhibiting a strong bias, it near-perfectly recovers the classification performance observed when training with corresponding unbiased data.
  arXiv  Detail & Related papers  (2022-05-26T12:18:48Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs [0.0699049312989311]
  We present a hybrid algorithm to estimate lung malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and radiomic features. We show that a model using image biomarkers alone is more accurate than one that combines biomarkers with volumetric radiomics, 3D CNNs, and semi-supervised learning.
  arXiv  Detail & Related papers  (2020-10-19T18:57:26Z)
• Mitosis Detection Under Limited Annotation: A Joint Learning Approach [5.117836409118142]
  Deep learning-based mitotic detection is on par with pathologists, but it requires large labeled data for training. We propose a deep classification framework for enhancing mitosis detection by leveraging class label information, via softmax loss, and spatial distribution information among samples, via distance metric learning. Our framework significantly improves the detection with small training data and achieves on par or superior performance compared to state-of-the-art methods for using the entire training data.
  arXiv  Detail & Related papers  (2020-06-17T10:46:29Z)

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.