Deep Convolutional Neural Network for Non-rigid Image Registration

• URL: http://arxiv.org/abs/2104.12034v1
• Date: Sat, 24 Apr 2021 23:24:29 GMT
• Title: Deep Convolutional Neural Network for Non-rigid Image Registration
• Authors: Eduard F. Durech
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Images taken at different times or positions undergo transformations such as rotation, scaling, skewing, and more. The process of aligning different images which have undergone transformations can be done via registration. Registration is desirable when analyzing time-series data for tracking, averaging, or differential diagnoses of diseases. Efficient registration methods exist for rigid (including linear or affine) transformations; however, for non-rigid (also known as non-affine) transformations, current methods are computationally expensive and time-consuming. 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.

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)
• Deep Dynamic Scene Deblurring from Optical Flow [53.625999196063574]
  Deblurring can provide visually more pleasant pictures and make photography more convenient. It is difficult to model the non-uniform blur mathematically. We develop a convolutional neural network (CNN) to restore the sharp images from the deblurred features.
  arXiv  Detail & Related papers  (2023-01-18T06:37:21Z)
• 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)
• Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning [11.795108660250843]
  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.
  arXiv  Detail & Related papers  (2022-06-25T08:34:59Z)
• Medical Image Registration via Neural Fields [35.80302878742334]
  We present a new neural net based image registration framework, called NIR (Neural Image Registration), which is based on optimization but utilizes deep neural nets to model deformations between image pairs. Experiments on two 3D MR brain scan datasets demonstrate that NIR yields state-of-the-art performance in terms of both registration accuracy and regularity, while running significantly faster than traditional optimization-based methods.
  arXiv  Detail & Related papers  (2022-06-07T08:43:31Z)
• 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)
• 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)
• Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks [11.4219428942199]
  We present a novel, efficient unsupervised symmetric image registration method. We evaluate our method on 3D image registration with a large scale brain image dataset.
  arXiv  Detail & Related papers  (2020-03-20T22:07:24Z)
• Computational optimization of convolutional neural networks using separated filters architecture [69.73393478582027]
  We consider a convolutional neural network transformation that reduces computation complexity and thus speedups neural network processing. Use of convolutional neural networks (CNN) is the standard approach to image recognition despite the fact they can be too computationally demanding.
  arXiv  Detail & Related papers  (2020-02-18T17:42:13Z)

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.