Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network

• URL: http://arxiv.org/abs/2506.19222v1
• Date: Tue, 24 Jun 2025 01:11:00 GMT
• Title: Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network
• Authors: Xinke Ma, Yongsheng Pan, Qingjie Zeng, Mengkang Lu, Bolysbek Murat Yerzhanuly, Bazargul Matkerim, Yong Xia,
• Abstract summary: We introduce a novel ROI-based registration approach named EASR-DCN.<n>Our method represents medical images through effective ROIs and achieves independent alignment of these ROIs without requiring labels.
• Score: 7.863948834044364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective representation of Regions of Interest (ROI) and independent alignment of these ROIs can significantly enhance the performance of deformable medical image registration (DMIR). However, current learning-based DMIR methods have limitations. Unsupervised techniques disregard ROI representation and proceed directly with aligning pairs of images, while weakly-supervised methods heavily depend on label constraints to facilitate registration. To address these issues, we introduce a novel ROI-based registration approach named EASR-DCN. Our method represents medical images through effective ROIs and achieves independent alignment of these ROIs without requiring labels. Specifically, we first used a Gaussian mixture model for intensity analysis to represent images using multiple effective ROIs with distinct intensities. Furthermore, we propose a novel Divide-and-Conquer Network (DCN) to process these ROIs through separate channels to learn feature alignments for each ROI. The resultant correspondences are seamlessly integrated to generate a comprehensive displacement vector field. Extensive experiments were performed on three MRI and one CT datasets to showcase the superior accuracy and deformation reduction efficacy of our EASR-DCN. Compared to VoxelMorph, our EASR-DCN achieved improvements of 10.31\% in the Dice score for brain MRI, 13.01\% for cardiac MRI, and 5.75\% for hippocampus MRI, highlighting its promising potential for clinical applications. The code for this work will be released upon acceptance of the paper.

Related papers

• A Steerable Deep Network for Model-Free Diffusion MRI Registration [4.813333335683418]
  We present a novel, deep learning framework for model-free, nonrigid registration of raw diffusion MRI data.<n>This work establishes a foundation for data-driven, geometry-aware dMRI registration directly in the acquisition space.
  arXiv  Detail & Related papers  (2025-01-08T19:18:44Z)
• Weakly supervised alignment and registration of MR-CT for cervical cancer radiotherapy [9.060365057476133]
  Cervical cancer is one of the leading causes of death in women. We propose a preliminary spatial alignment algorithm and a weakly supervised multimodal registration network.
  arXiv  Detail & Related papers  (2024-05-21T15:05:51Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging [59.78761085714715]
  This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework for liver lesion classification. The proposed framework has been validated through comprehensive experiments on two clinical datasets. To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public.
  arXiv  Detail & Related papers  (2024-02-27T06:32:56Z)
• Multi-Objective Learning for Deformable Image Registration [0.0]
  Deformable image registration (DIR) involves optimization of multiple conflicting objectives. In this paper, we combine a recently proposed approach for MO training of neural networks with a well-known deep neural network for DIR. We evaluate the proposed approach for DIR of pelvic magnetic resonance imaging (MRI) scans.
  arXiv  Detail & Related papers  (2024-02-23T15:42:13Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Explainable unsupervised multi-modal image registration using deep networks [2.197364252030876]
  MRI image registration aims to geometrically 'pair' diagnoses from different modalities, time points and slices. In this work, we show that our DL model becomes fully explainable, setting the framework to generalise our approach on further medical imaging data.
  arXiv  Detail & Related papers  (2023-08-03T19:13:48Z)
• Recurrence With Correlation Network for Medical Image Registration [66.63200823918429]
  We present Recurrence with Correlation Network (RWCNet), a medical image registration network with multi-scale features and a cost volume layer. We demonstrate that these architectural features improve medical image registration accuracy in two image registration datasets.
  arXiv  Detail & Related papers  (2023-02-05T02:41:46Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• A Deep Discontinuity-Preserving Image Registration Network [73.03885837923599]
  Most deep learning-based registration methods assume that the desired deformation fields are globally smooth and continuous. We propose a weakly-supervised Deep Discontinuity-preserving Image Registration network (DDIR) to obtain better registration performance and realistic deformation fields. We demonstrate that our method achieves significant improvements in registration accuracy and predicts more realistic deformations, in registration experiments on cardiac magnetic resonance (MR) images.
  arXiv  Detail & Related papers  (2021-07-09T13:35:59Z)
• F3RNet: Full-Resolution Residual Registration Network for Deformable Image Registration [21.99118499516863]
  Deformable image registration (DIR) is essential for many image-guided therapies. We propose a novel unsupervised registration network, namely the Full-Resolution Residual Registration Network (F3RNet) One stream takes advantage of the full-resolution information that facilitates accurate voxel-level registration. The other stream learns the deep multi-scale residual representations to obtain robust recognition.
  arXiv  Detail & Related papers  (2020-09-15T15:05:54Z)

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.