Hybrid Atlas Building with Deep Registration Priors

• URL: http://arxiv.org/abs/2112.06406v1
• Date: Mon, 13 Dec 2021 03:55:27 GMT
• Title: Hybrid Atlas Building with Deep Registration Priors
• Authors: Nian Wu, Jian Wang, Miaomiao Zhang, Guixu Zhang, Yaxin Peng and Chaomin Shen
• Abstract summary: We introduce a novel hybrid atlas building algorithm that fast estimates atlas from large-scale image datasets with much reduced computational cost. We demonstrate the effectiveness of this proposed model on 3D brain magnetic resonance imaging (MRI) scans.
• Score: 22.744067458133628
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Registration-based atlas building often poses computational challenges in high-dimensional image spaces. In this paper, we introduce a novel hybrid atlas building algorithm that fast estimates atlas from large-scale image datasets with much reduced computational cost. In contrast to previous approaches that iteratively perform registration tasks between an estimated atlas and individual images, we propose to use learned priors of registration from pre-trained neural networks. This newly developed hybrid framework features several advantages of (i) providing an efficient way of atlas building without losing the quality of results, and (ii) offering flexibility in utilizing a wide variety of deep learning based registration methods. We demonstrate the effectiveness of this proposed model on 3D brain magnetic resonance imaging (MRI) scans.

Related papers

• Learning KAN-based Implicit Neural Representations for Deformable Image Registration [0.8037525071177886]
  We propose KAN-IDIR and RandKAN-IDIR, the first integration of Kolmogorov-Arnold Networks (KANs) into deformable image registration with implicit neural representations (INRs)<n>Our proposed randomized basis sampling strategy reduces the required number of basis functions in KAN while maintaining registration quality.<n>KAN-IDIR and RandKAN-IDIR achieved the highest accuracy among INR-based methods across all evaluated modalities and anatomies.
  arXiv  Detail & Related papers  (2025-09-26T19:37:46Z)
• Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction [48.30341580103962]
  We propose a novel re-visible dual-domain self-supervised deep unfolding network to address these issues. We design a deep unfolding network based on Chambolle and Pock Proximal Point Algorithm (DUN-CP-PPA) to achieve end-to-end reconstruction. Experiments conducted on the fastMRI and IXI datasets demonstrate that our method significantly outperforms state-of-the-art approaches in terms of reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-07T12:29:32Z)
• Two Approaches to Supervised Image Segmentation [55.616364225463066]
  The present work develops comparison experiments between deep learning and multiset neurons approaches. The deep learning approach confirmed its potential for performing image segmentation. The alternative multiset methodology allowed for enhanced accuracy while requiring little computational resources.
  arXiv  Detail & Related papers  (2023-07-19T16:42:52Z)
• Neural Maximum A Posteriori Estimation on Unpaired Data for Motion Deblurring [87.97330195531029]
  We propose a Neural Maximum A Posteriori (NeurMAP) estimation framework for training neural networks to recover blind motion information and sharp content from unpaired data. The proposed NeurMAP is an approach to existing deblurring neural networks, and is the first framework that enables training image deblurring networks on unpaired datasets.
  arXiv  Detail & Related papers  (2022-04-26T08:09:47Z)
• Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning with Pairwise Alignment [18.338563869053065]
  This work explores using a convolutional neural network (CNN) to jointly predict the atlas and a stationary velocity field (SVF) parameterization for diffeomorphic image registration. Our results show that the proposed framework achieves better performance than other state-of-the-art image registration algorithms.
  arXiv  Detail & Related papers  (2022-02-07T23:52:21Z)
• PARCEL: Physics-based unsupervised contrastive representation learning for parallel MR imaging [9.16860702327751]
  This paper proposes a physics based unsupervised contrastive representation learning (PARCEL) method to speed up parallel MR imaging. Specifically, PARCEL has three key ingredients to achieve direct deep learning from the undersampled k-space data. A specially designed co-training loss is designed to guide the two networks to capture the inherent features and representations of the MR image.
  arXiv  Detail & Related papers  (2022-02-03T10:09:19Z)
• Self-Denoising Neural Networks for Few Shot Learning [66.38505903102373]
  We present a new training scheme that adds noise at multiple stages of an existing neural architecture while simultaneously learning to be robust to this added noise. This architecture, which we call a Self-Denoising Neural Network (SDNN), can be applied easily to most modern convolutional neural architectures.
  arXiv  Detail & Related papers  (2021-10-26T03:28:36Z)
• Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks [11.677800377183972]
  We propose Atlas-ISTN, a framework that jointly learns segmentation and registration on 2D and 3D image data. Atlas-ISTN learns to segment multiple structures of interest and to register the constructed, topologically consistent atlas labelmap to an intermediate pixel-wise segmentation. This process both mitigates for noise in the target image that can result in spurious pixel-wise predictions, as well as improves upon the one-pass prediction of the model.
  arXiv  Detail & Related papers  (2020-12-18T21:53:09Z)
• Sparse Signal Models for Data Augmentation in Deep Learning ATR [0.8999056386710496]
  We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm. We exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting.
  arXiv  Detail & Related papers  (2020-12-16T21:46:33Z)
• NAS-DIP: Learning Deep Image Prior with Neural Architecture Search [65.79109790446257]
  Recent work has shown that the structure of deep convolutional neural networks can be used as a structured image prior. We propose to search for neural architectures that capture stronger image priors. We search for an improved network by leveraging an existing neural architecture search algorithm.
  arXiv  Detail & Related papers  (2020-08-26T17:59:36Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)
• DeepFLASH: An Efficient Network for Learning-based Medical Image Registration [8.781861951759948]
  DeepFLASH is a novel network with efficient training and inference for learning-based medical image registration. We demonstrate our algorithm in two different applications of image registration: 2D synthetic data and 3D real brain magnetic resonance (MR) images.
  arXiv  Detail & Related papers  (2020-04-05T05:17:07Z)

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.