FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Registration

• URL: http://arxiv.org/abs/2404.01249v1
• Date: Mon, 1 Apr 2024 17:12:47 GMT
• Title: FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Registration
• Authors: Rohit Jena, Pratik Chaudhari, James C. Gee,
• Abstract summary: Diffeomorphic Image Registration is a critical part of the analysis in various imaging modalities and downstream tasks like image translation, segmentation, and atlas building. Our proposed framework leads to a consistent improvement in performance, and from 300x up to 2000x speedup over existing algorithms. Our modular library design makes it easy to use and allows customization via user-defined cost functions.
• Score: 20.34181966545357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffeomorphic Image Registration is a critical part of the analysis in various imaging modalities and downstream tasks like image translation, segmentation, and atlas building. Registration algorithms based on optimization have stood the test of time in terms of accuracy, reliability, and robustness across a wide spectrum of modalities and acquisition settings. However, these algorithms converge slowly, are prohibitively expensive to run, and their usage requires a steep learning curve, limiting their scalability to larger clinical and scientific studies. In this paper, we develop multi-scale Adaptive Riemannian Optimization algorithms for diffeomorphic image registration. We demonstrate compelling improvements on image registration across a spectrum of modalities and anatomies by measuring structural and landmark overlap of the registered image volumes. Our proposed framework leads to a consistent improvement in performance, and from 300x up to 2000x speedup over existing algorithms. Our modular library design makes it easy to use and allows customization via user-defined cost functions.

Related papers

• Deep Implicit Optimization for Robust and Flexible Image Registration [20.34181966545357]
  We bridge the gap between classical and learning methods by incorporating optimization as a layer in a deep network. By implicitly differentiating end-to-end through an iterative optimization solver, our learned features are registration and label-aware. Our framework shows excellent performance on in-domain datasets, and is agnostic to domain shift.
  arXiv  Detail & Related papers  (2024-06-11T15:28:48Z)
• Optimize and Reduce: A Top-Down Approach for Image Vectorization [12.998637003026273]
  We propose Optimize & Reduce (O&R), a top-down approach to vectorization that is both fast and domain-agnostic. O&R aims to attain a compact representation of input images by iteratively optimizing B'ezier curve parameters. We demonstrate that our method is domain agnostic and outperforms existing works in both reconstruction and perceptual quality for a fixed number of shapes.
  arXiv  Detail & Related papers  (2023-12-18T16:41:03Z)
• SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation [19.683682147655496]
  In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration building on top of a Self-supervised Anatomical eMbedding algorithm. We name our approach SAM-Enhanced registration (SAME++), which decomposes image registration into four steps: affine transformation, coarse deformation, deep non-parametric transformation, and instance optimization. As a complete registration framework, SAME++ markedly outperforms leading methods by $4.2%$ - $8.2%$ in terms of Dice score.
  arXiv  Detail & Related papers  (2023-11-25T10:11:04Z)
• Linearization Algorithms for Fully Composite Optimization [61.20539085730636]
  This paper studies first-order algorithms for solving fully composite optimization problems convex compact sets. We leverage the structure of the objective by handling differentiable and non-differentiable separately, linearizing only the smooth parts.
  arXiv  Detail & Related papers  (2023-02-24T18:41:48Z)
• Bridging Synthetic and Real Images: a Transferable and Multiple Consistency aided Fundus Image Enhancement Framework [61.74188977009786]
  We propose an end-to-end optimized teacher-student framework to simultaneously conduct image enhancement and domain adaptation. We also propose a novel multi-stage multi-attention guided enhancement network (MAGE-Net) as the backbones of our teacher and student network.
  arXiv  Detail & Related papers  (2023-02-23T06:16:15Z)
• Anatomy-aware and acquisition-agnostic joint registration with SynthMorph [6.017634371712142]
  Affine image registration is a cornerstone of medical image analysis. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use DL tool for joint affine-deformable registration of any brain image.
  arXiv  Detail & Related papers  (2023-01-26T18:59:33Z)
• Deep Diversity-Enhanced Feature Representation of Hyperspectral Images [87.47202258194719]
  We rectify 3D convolution by modifying its topology to enhance the rank upper-bound. We also propose a novel diversity-aware regularization (DA-Reg) term that acts on the feature maps to maximize independence among elements. To demonstrate the superiority of the proposed Re$3$-ConvSet and DA-Reg, we apply them to various HS image processing and analysis tasks.
  arXiv  Detail & Related papers  (2023-01-15T16:19:18Z)
• Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions [69.6849409155959]
  This paper proposes an automated learning registration algorithm (AutoReg) that cooperatively optimize both architectures and their corresponding training objectives. We conduct image registration experiments on multi-site volume datasets and various registration tasks. Our results show that our AutoReg may automatically learn an optimal deep registration network for given volumes and achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-14T01:54:38Z)
• Image-specific Convolutional Kernel Modulation for Single Image Super-resolution [85.09413241502209]
  In this issue, we propose a novel image-specific convolutional modulation kernel (IKM) We exploit the global contextual information of image or feature to generate an attention weight for adaptively modulating the convolutional kernels. Experiments on single image super-resolution show that the proposed methods achieve superior performances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-16T11:05:10Z)
• Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence [41.061667361696465]
  We introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM) Our method sets a new state of the art on standard benchmarks for semantic visual correspondence, proving its strong robustness to challenging intra-class variations.
  arXiv  Detail & Related papers  (2021-09-11T08:39:41Z)
• SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients [99.13839450032408]
  It is desired to design a universal framework for adaptive algorithms to solve general problems. In particular, our novel framework provides adaptive methods under non convergence support for setting.
  arXiv  Detail & Related papers  (2021-06-15T15:16:28Z)
• Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator [75.05106948314956]
  We show that an increasing large momentum parameter for the first-order moment is sufficient for adaptive scaling. We also give insights for increasing the momentum in a stagewise manner in accordance with stagewise decreasing step size.
  arXiv  Detail & Related papers  (2021-04-30T08:50:24Z)
• Convolutional Hough Matching Networks [39.524998833064956]
  We introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM) We cast it into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters. Our method sets a new state of the art on standard benchmarks for semantic visual correspondence, proving its strong robustness to challenging intra-class variations.
  arXiv  Detail & Related papers  (2021-03-31T06:17:03Z)
• Evolutionary Variational Optimization of Generative Models [0.0]
  We combine two popular optimization approaches to derive learning algorithms for generative models: variational optimization and evolutionary algorithms. We show that evolutionary algorithms can effectively and efficiently optimize the variational bound. In the category of "zero-shot" learning, we observed the evolutionary variational algorithm to significantly improve the state-of-the-art in many benchmark settings.
  arXiv  Detail & Related papers  (2020-12-22T19:06:33Z)
• A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding [57.1077544780653]
  We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end.
  arXiv  Detail & Related papers  (2020-06-26T08:34:54Z)
• Convergence of adaptive algorithms for weakly convex constrained optimization [59.36386973876765]
  We prove the $mathcaltilde O(t-1/4)$ rate of convergence for the norm of the gradient of Moreau envelope. Our analysis works with mini-batch size of $1$, constant first and second order moment parameters, and possibly smooth optimization domains.
  arXiv  Detail & Related papers  (2020-06-11T17:43:19Z)
• A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image Segmentation [2.6381163133447836]
  We present a class of piecewise-constant image segmentation models that incorporate a difference of anisotropic and isotropic total variation. In addition, a generalization to color image segmentation is discussed.
  arXiv  Detail & Related papers  (2020-05-09T09:35:44Z)
• 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.