Evaluation of Alignment-Regularity Characteristics in Deformable Image Registration

• URL: http://arxiv.org/abs/2503.07185v1
• Date: Mon, 10 Mar 2025 11:10:35 GMT
• Title: Evaluation of Alignment-Regularity Characteristics in Deformable Image Registration
• Authors: Vasiliki Sideri-Lampretsa, Daniel Rueckert, Huaqi Qiu,
• Abstract summary: evaluating deformable image registration (DIR) is challenging due to the inherent trade-off between achieving high alignment accuracy and maintaining deformation regularity.<n>We introduce a novel evaluation scheme based on the alignment-regularity characteristic (ARC) to systematically capture and analyze this trade-off.
• Score: 11.644368003959682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating deformable image registration (DIR) is challenging due to the inherent trade-off between achieving high alignment accuracy and maintaining deformation regularity. In this work, we introduce a novel evaluation scheme based on the alignment-regularity characteristic (ARC) to systematically capture and analyze this trade-off. We first introduce the ARC curves, which describe the performance of a given registration algorithm as a spectrum measured by alignment and regularity metrics. We further adopt a HyperNetwork-based approach that learns to continuously interpolate across the full regularization range, accelerating the construction and improving the sample density of ARC curves. We empirically demonstrate our evaluation scheme using representative learning-based deformable image registration methods with various network architectures and transformation models on two public datasets. We present a range of findings not evident from existing evaluation practices and provide general recommendations for model evaluation and selection using our evaluation scheme. All code relevant is made publicly available.

Related papers

• Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks [5.479932919974457]
  This paper proposes a novel unsupervised learning-based fully convolutional network (FCN) framework for fast diffeomorphic image registration. Experiments are conducted on three distinct T1-weighted magnetic resonance imaging (T1w MRI) datasets.
  arXiv  Detail & Related papers  (2024-04-05T17:46:38Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Learning for Transductive Threshold Calibration in Open-World Recognition [83.35320675679122]
  We introduce OpenGCN, a Graph Neural Network-based transductive threshold calibration method with enhanced robustness and adaptability. Experiments across open-world visual recognition benchmarks validate OpenGCN's superiority over existing posthoc calibration methods for open-world threshold calibration.
  arXiv  Detail & Related papers  (2023-05-19T23:52:48Z)
• Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration [11.498623409184225]
  We introduce an end-to-end framework that uses neural networks to learn a deformation regularizer directly from data. The proposed method is built upon a Transformer-based model, but it can be readily adapted to any network architecture.
  arXiv  Detail & Related papers  (2023-03-10T19:11:16Z)
• GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond [101.5329678997916]
  We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making. We propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR.
  arXiv  Detail & Related papers  (2022-11-03T16:42:40Z)
• Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition [54.23513799338309]
  We present an Adaptive Local-Component-aware Graph Convolutional Network for skeleton-based action recognition. Our method provides a stronger representation than the global embedding and helps our model reach state-of-the-art.
  arXiv  Detail & Related papers  (2022-09-21T02:33:07Z)
• A Deep-Discrete Learning Framework for Spherical Surface Registration [4.7633236054762875]
  Cortical surface registration is a fundamental tool for neuroimaging analysis. We propose a novel unsupervised learning-based framework that converts registration to a multi-label classification problem. Experiments show that our proposed framework performs competitively, in terms of similarity and areal distortion, relative to the most popular classical surface registration algorithms.
  arXiv  Detail & Related papers  (2022-03-24T11:47:11Z)
• Heterogeneous Calibration: A post-hoc model-agnostic framework for improved generalization [8.815439276597818]
  We introduce the notion of heterogeneous calibration that applies a post-hoc model-agnostic transformation to model outputs for improving AUC performance on binary classification tasks. We refer to simple patterns as heterogeneous partitions of the feature space and show theoretically that perfectly calibrating each partition separately optimize AUC. While the theoretical optimality of this framework holds for any model, we focus on deep neural networks (DNNs) and test the simplest instantiation of this paradigm on a variety of open-source datasets.
  arXiv  Detail & Related papers  (2022-02-10T05:08:50Z)
• GAN-Supervised Dense Visual Alignment [95.37027391102684]
  We propose GAN-Supervised Learning, a framework for learning discriminative models and their GAN-generated training data jointly end-to-end. Inspired by the classic Congealing method, our GANgealing algorithm trains a Spatial Transformer to map random samples from a GAN trained on unaligned data to a common, jointly-learned target mode.
  arXiv  Detail & Related papers  (2021-12-09T18:59:58Z)
• Understanding Dynamics of Nonlinear Representation Learning and Its Application [12.697842097171119]
  We study the dynamics of implicit nonlinear representation learning. We show that the data-architecture alignment condition is sufficient for the global convergence. We derive a new training framework, which satisfies the data-architecture alignment condition without assuming it.
  arXiv  Detail & Related papers  (2021-06-28T16:31:30Z)
• An Adaptive Framework for Learning Unsupervised Depth Completion [59.17364202590475]
  We present a method to infer a dense depth map from a color image and associated sparse depth measurements. We show that regularization and co-visibility are related via the fitness of the model to data and can be unified into a single framework.
  arXiv  Detail & Related papers  (2021-06-06T02:27:55Z)
• Out-of-distribution Generalization via Partial Feature Decorrelation [72.96261704851683]
  We present a novel Partial Feature Decorrelation Learning (PFDL) algorithm, which jointly optimize a feature decomposition network and the target image classification model. The experiments on real-world datasets demonstrate that our method can improve the backbone model's accuracy on OOD image classification datasets.
  arXiv  Detail & Related papers  (2020-07-30T05:48:48Z)

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.