Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning

• URL: http://arxiv.org/abs/2206.12596v1
• Date: Sat, 25 Jun 2022 08:34:59 GMT
• Title: Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning
• Authors: Mingyuan Meng, Lei Bi, Dagan Feng, and Jinman Kim
• Abstract summary: We propose a Non-Iterative Coarse-to-finE registration network (NICE-Net) for deformable image registration. NICE-Net can outperform state-of-the-art iterative deep registration methods while only requiring similar runtime to non-iterative methods.
• Score: 11.795108660250843
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deformable image registration is a crucial step in medical image analysis for finding a non-linear spatial transformation between a pair of fixed and moving images. Deep registration methods based on Convolutional Neural Networks (CNNs) have been widely used as they can perform image registration in a fast and end-to-end manner. However, these methods usually have limited performance for image pairs with large deformations. Recently, iterative deep registration methods have been used to alleviate this limitation, where the transformations are iteratively learned in a coarse-to-fine manner. However, iterative methods inevitably prolong the registration runtime, and tend to learn separate image features for each iteration, which hinders the features from being leveraged to facilitate the registration at later iterations. In this study, we propose a Non-Iterative Coarse-to-finE registration Network (NICE-Net) for deformable image registration. In the NICE-Net, we propose: (i) a Single-pass Deep Cumulative Learning (SDCL) decoder that can cumulatively learn coarse-to-fine transformations within a single pass (iteration) of the network, and (ii) a Selectively-propagated Feature Learning (SFL) encoder that can learn common image features for the whole coarse-to-fine registration process and selectively propagate the features as needed. Extensive experiments on six public datasets of 3D brain Magnetic Resonance Imaging (MRI) show that our proposed NICE-Net can outperform state-of-the-art iterative deep registration methods while only requiring similar runtime to non-iterative methods.

Related papers

• Deep Multi-Threshold Spiking-UNet for Image Processing [51.88730892920031]
  This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. Experimental results show that, on image segmentation and denoising, our Spiking-UNet achieves comparable performance to its non-spiking counterpart.
  arXiv  Detail & Related papers  (2023-07-20T16:00:19Z)
• Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration [10.994223928445589]
  We propose a Non-Iterative Coarse-to-finE Transformer network (NICE-Trans) for image registration. Our NICE-Trans is the first deep registration method that performs joint affine and deformable coarse-to-fine registration within a single network.
  arXiv  Detail & Related papers  (2023-07-07T07:07:42Z)
• ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration [20.054872823030454]
  Deformable image registration serves as an essential preprocessing step for neuroimaging data. We propose a novel solution, called Anti-Blur Network (ABN), for multi-stage image registration.
  arXiv  Detail & Related papers  (2022-12-06T19:21:43Z)
• Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images [74.99415008543276]
  Most deep learning-based registration methods assume that the deformation fields are smooth and continuous everywhere in the image domain. We propose a novel discontinuity-preserving image registration method to tackle this challenge, which ensures globally discontinuous and locally smooth deformation fields. A co-attention block is proposed in the segmentation component of the network to learn the structural correlations in the input images. We evaluate our method on the task of intra-subject-temporal image registration using large-scale cinematic cardiac magnetic resonance image sequences.
  arXiv  Detail & Related papers  (2022-11-24T23:45:01Z)
• Affine Medical Image Registration with Coarse-to-Fine Vision Transformer [11.4219428942199]
  We present a learning-based algorithm, Coarse-to-Fine Vision Transformer (C2FViT), for 3D affine medical image registration. Our method is superior to the existing CNNs-based affine registration methods in terms of registration accuracy, robustness and generalizability.
  arXiv  Detail & Related papers  (2022-03-29T03:18:43Z)
• Learning a Model-Driven Variational Network for Deformable Image Registration [89.9830129923847]
  VR-Net is a novel cascaded variational network for unsupervised deformable image registration. It outperforms state-of-the-art deep learning methods on registration accuracy. It maintains the fast inference speed of deep learning and the data-efficiency of variational model.
  arXiv  Detail & Related papers  (2021-05-25T21:37:37Z)
• Deep Convolutional Neural Network for Non-rigid Image Registration [0.0]
  In this report, I will explore the ability of a deep neural network (DNN) and, more specifically, a deep convolutional neural network (CNN) to efficiently perform non-rigid image registration. The experimental results show that a CNN can be used for efficient non-rigid image registration and in significantly less computational time than a conventional Diffeomorphic Demons or Pyramiding approach.
  arXiv  Detail & Related papers  (2021-04-24T23:24:29Z)
• Test-Time Training for Deformable Multi-Scale Image Registration [15.523457398508263]
  Deep learning-based registration approaches such as VoxelMorph have been emerging and achieve competitive performance. We construct a test-time training for deep deformable image registration to improve the generalization ability of conventional learning-based registration model.
  arXiv  Detail & Related papers  (2021-03-25T03:22:59Z)
• Learning to Learn Parameterized Classification Networks for Scalable Input Images [76.44375136492827]
  Convolutional Neural Networks (CNNs) do not have a predictable recognition behavior with respect to the input resolution change. We employ meta learners to generate convolutional weights of main networks for various input scales. We further utilize knowledge distillation on the fly over model predictions based on different input resolutions.
  arXiv  Detail & Related papers  (2020-07-13T04:27:25Z)
• An Auto-Context Deformable Registration Network for Infant Brain MRI [54.57017031561516]
  We propose an infant-dedicated deep registration network that uses the auto-context strategy to gradually refine the deformation fields. Our method estimates the deformation fields by invoking a single network multiple times for iterative deformation refinement. Experimental results in comparison with state-of-the-art registration methods indicate that our method achieves higher accuracy while at the same time preserves the smoothness of the deformation fields.
  arXiv  Detail & Related papers  (2020-05-19T06:00:13Z)
• 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)

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.