cIDIR: Conditioned Implicit Neural Representation for Regularized Deformable Image Registration

• URL: http://arxiv.org/abs/2507.12953v1
• Date: Thu, 17 Jul 2025 09:48:53 GMT
• Title: cIDIR: Conditioned Implicit Neural Representation for Regularized Deformable Image Registration
• Authors: Sidaty El Hadramy, Oumeymah Cherkaoui, Philippe C. Cattin,
• Abstract summary: We propose cIDI, a novel deformable image registration framework based on Implicit Neural Representations (INRs)<n>CIDI is trained over a prior distribution of regularization hyper parameters, then optimized over them by using the segmentations masks as an observation.<n>It achieves high accuracy and robustness across the dataset.
• Score: 0.7022492404644499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Regularization is essential in deformable image registration (DIR) to ensure that the estimated Deformation Vector Field (DVF) remains smooth, physically plausible, and anatomically consistent. However, fine-tuning regularization parameters in learning-based DIR frameworks is computationally expensive, often requiring multiple training iterations. To address this, we propose cIDI, a novel DIR framework based on Implicit Neural Representations (INRs) that conditions the registration process on regularization hyperparameters. Unlike conventional methods that require retraining for each regularization hyperparameter setting, cIDIR is trained over a prior distribution of these hyperparameters, then optimized over the regularization hyperparameters by using the segmentations masks as an observation. Additionally, cIDIR models a continuous and differentiable DVF, enabling seamless integration of advanced regularization techniques via automatic differentiation. Evaluated on the DIR-LAB dataset, $\operatorname{cIDIR}$ achieves high accuracy and robustness across the dataset.

Related papers

• SDTN and TRN: Adaptive Spectral-Spatial Feature Extraction for Hyperspectral Image Classification [1.2871580250533408]
  Hyperspectral image classification plays a pivotal role in precision agriculture, providing accurate insights into crop health monitoring, disease detection, and soil analysis.<n>Traditional methods struggle with high-dimensional data, spectral-spatial redundancy, and the scarcity of labeled samples, often leading to suboptimal performance.<n>To address these challenges, we propose the Self-Adaptive- Regularized Network (SDTN), which combines tensor decomposition with regularization mechanisms to dynamically adjust tensor ranks.<n>This approach not only maintains high classification accuracy but also significantly reduces computational complexity, making the framework highly suitable for real-time deployment in resource-constrained environments.
  arXiv  Detail & Related papers  (2025-07-13T04:53:33Z)
• Towards Initialization-Agnostic Clustering with Iterative Adaptive Resonance Theory [8.312275539092466]
  Iterative Refinement Adaptive Resonance Theory (IR-ART) integrates three key phases into a unified iterative framework.<n> IR-ART improves tolerance to suboptimal vigilance parameter values while preserving the parameter simplicity of Fuzzy ART.<n>Case studies visually confirm the algorithm's self-optimization capability through iterative refinement.
  arXiv  Detail & Related papers  (2025-05-07T14:12:39Z)
• Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems [49.819436680336786]
  We propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems.<n>Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive implicit process prior that captures complex, non-stationary transition dynamics.<n>Our ETGPSSM outperforms existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2025-03-24T03:19:45Z)
• RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration [2.879328762187361]
  We present RAAD-LLM, a novel framework for adaptive anomaly detection.<n>By effectively utilizing domain-specific knowledge, RAAD-LLM enhances the detection of anomalies in time series data.<n>Results show significant improvements over our previous model with an accuracy increase from 70.7% to 88.6% on the real-world dataset.
  arXiv  Detail & Related papers  (2025-03-04T17:20:43Z)
• Towards Generalizable Trajectory Prediction Using Dual-Level Representation Learning And Adaptive Prompting [107.4034346788744]
  Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions.<n>We propose Perceiver with Register queries (PerReg+), a novel trajectory prediction framework that introduces: (1) Dual-Level Representation Learning via Self-Distillation (SD) and Masked Reconstruction (MR), capturing global context and fine-grained details; (2) Enhanced Multimodality using register-based queries and pretraining, eliminating the need for clustering and suppression; and (3) Adaptive Prompt Tuning during fine-tuning, freezing the main architecture and optimizing a small number of prompts for efficient adaptation.
  arXiv  Detail & Related papers  (2025-01-08T20:11:09Z)
• Unsupervised learning of spatially varying regularization for diffeomorphic image registration [10.748299084301626]
  We propose a hierarchical probabilistic model that integrates a prior distribution on the deformation regularization strength.<n>The proposed method is straightforward to implement and easily integrates with various registration network architectures.
  arXiv  Detail & Related papers  (2024-12-23T21:01:32Z)
• Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model [66.91323540178739]
  Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior. We revisit SR from a novel information-theoretic perspective and find that sequential modeling methods fail to adequately capture randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests.
  arXiv  Detail & Related papers  (2024-10-31T14:52:01Z)
• Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining [50.00291020618743]
  This work introduces a novel, parameter-adaptive AMPC architecture capable of online tuning without recomputing large datasets and retraining. We showcase the effectiveness of parameter-adaptive AMPC by controlling the swing-ups of two different real cartpole systems with a severely resource-constrained microcontroller (MCU) Taken together, these contributions represent a marked step toward the practical application of AMPC in real-world systems.
  arXiv  Detail & Related papers  (2024-04-08T20:02:19Z)
• GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration [62.41725951450803]
  Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the field. We construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyper parameter-free balance on multiple losses. Our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference.
  arXiv  Detail & Related papers  (2023-06-26T13:32:09Z)
• Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer [60.31021888394358]
  Unsupervised Domain Adaptation (UDA) can effectively address domain gap issues in real-world image Super-Resolution (SR) We propose a SOurce-free Domain Adaptation framework for image SR (SODA-SR) to address this issue, i.e., adapt a source-trained model to a target domain with only unlabeled target data.
  arXiv  Detail & Related papers  (2023-03-31T03:14:44Z)
• Conditional Deformable Image Registration with Spatially-Variant and Adaptive Regularization [2.3419031955865517]
  We propose a learning-based registration approach based on a novel conditional spatially adaptive instance normalization (CSAIN) Experiments show that our proposed method outperforms the baseline approaches while achieving spatially-variant and adaptive regularization.
  arXiv  Detail & Related papers  (2023-03-19T16:12:06Z)
• Conditional Deformable Image Registration with Convolutional Neural Network [15.83842747998493]
  We propose a conditional image registration method and a new self-supervised learning paradigm for deep deformable image registration. Our proposed method enables the precise control of the smoothness of the deformation field without sacrificing the runtime advantage or registration accuracy.
  arXiv  Detail & Related papers  (2021-06-23T22:25:28Z)

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.