LDM-Morph: Latent diffusion model guided deformable image registration

• URL: http://arxiv.org/abs/2411.15426v1
• Date: Sat, 23 Nov 2024 03:04:36 GMT
• Title: LDM-Morph: Latent diffusion model guided deformable image registration
• Authors: Jiong Wu, Kuang Gong,
• Abstract summary: We propose LDM-Morph, an unsupervised deformable registration algorithm for medical image registration. LDM-Morph integrated features extracted from the latent diffusion model (LDM) to enrich the semantic information. Extensive experiments on four public 2D cardiac image datasets show that the proposed LDM-Morph framework outperformed existing state-of-the-art CNNs- and Transformers-based registration methods.
• Score: 2.8195553455247317
• License:
• Abstract: Deformable image registration plays an essential role in various medical image tasks. Existing deep learning-based deformable registration frameworks primarily utilize convolutional neural networks (CNNs) or Transformers to learn features to predict the deformations. However, the lack of semantic information in the learned features limits the registration performance. Furthermore, the similarity metric of the loss function is often evaluated only in the pixel space, which ignores the matching of high-level anatomical features and can lead to deformation folding. To address these issues, in this work, we proposed LDM-Morph, an unsupervised deformable registration algorithm for medical image registration. LDM-Morph integrated features extracted from the latent diffusion model (LDM) to enrich the semantic information. Additionally, a latent and global feature-based cross-attention module (LGCA) was designed to enhance the interaction of semantic information from LDM and global information from multi-head self-attention operations. Finally, a hierarchical metric was proposed to evaluate the similarity of image pairs in both the original pixel space and latent-feature space, enhancing topology preservation while improving registration accuracy. Extensive experiments on four public 2D cardiac image datasets show that the proposed LDM-Morph framework outperformed existing state-of-the-art CNNs- and Transformers-based registration methods regarding accuracy and topology preservation with comparable computational efficiency. Our code is publicly available at https://github.com/wujiong-hub/LDM-Morph.

Related papers

• Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation [54.96563068182733]
  We propose Modality Adaptation with text-to-image Diffusion Models (MADM) for semantic segmentation task. MADM utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model's cross-modality capabilities. We show that MADM achieves state-of-the-art adaptation performance across various modality tasks, including images to depth, infrared, and event modalities.
  arXiv  Detail & Related papers  (2024-10-29T03:49:40Z)
• MsMorph: An Unsupervised pyramid learning network for brain image registration [4.000367245594772]
  MsMorph is an image registration framework aimed at mimicking the manual process of registering image pairs. It decodes semantic information at different scales and continuously compen-sates for the predicted deformation field. The proposed method simulates the manual approach to registration, focusing on different regions of the image pairs and their neighborhoods.
  arXiv  Detail & Related papers  (2024-10-23T19:20:57Z)
• NestedMorph: Enhancing Deformable Medical Image Registration with Nested Attention Mechanisms [0.0]
  Deformable image registration is crucial for aligning medical images in a non-linear fashion across different modalities. This paper presents NestedMorph, a novel network utilizing a Nested Attention Fusion approach to improve intra-subject deformable registration.
  arXiv  Detail & Related papers  (2024-10-03T14:53:42Z)
• Dual-Attention Frequency Fusion at Multi-Scale for Joint Segmentation and Deformable Medical Image Registration [2.6089354079273512]
  We propose a multi-task learning framework based on dual attention frequency fusion (DAFF-Net) DAFF-Net simultaneously achieves the segmentation masks and dense deformation fields in a single-step estimation. Experiments on three public 3D brain magnetic resonance imaging (MRI) datasets demonstrate that the proposed DAFF-Net and its unsupervised variant outperform state-of-the-art registration methods.
  arXiv  Detail & Related papers  (2024-09-29T11:11:04Z)
• Progressive Retinal Image Registration via Global and Local Deformable Transformations [49.032894312826244]
  We propose a hybrid registration framework called HybridRetina. We use a keypoint detector and a deformation network called GAMorph to estimate the global transformation and local deformable transformation. Experiments on two widely-used datasets, FIRE and FLoRI21, show that our proposed HybridRetina significantly outperforms some state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-02T08:43:50Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• FSDiffReg: Feature-wise and Score-wise Diffusion-guided Unsupervised Deformable Image Registration for Cardiac Images [12.20081061919718]
  Unsupervised deformable image registration is one of the challenging tasks in medical imaging. We present two modules: Feature-wise Diffusion-Guided Module and Score-wise Diffusion-Guided Module. Experiment results on the 3D medical cardiac image registration task validate our model's ability to provide refined deformation fields with preserved topology effectively.
  arXiv  Detail & Related papers  (2023-07-22T10:09:22Z)
• ACSGRegNet: A Deep Learning-based Framework for Unsupervised Joint Affine and Diffeomorphic Registration of Lumbar Spine CT via Cross- and Self-Attention Fusion [4.068962439293273]
  This study proposes a novel end-to-end deep learning-based framework for medical image registration. ACSGRegNet integrates a cross-attention module for establishing inter-image feature correspondences and a self-attention module for intra-image anatomical structures aware. Our method achieved an average Dice of 0.963 and an average distance error of 0.321mm, which are better than the state-of-the-art (SOTA)
  arXiv  Detail & Related papers  (2022-08-04T13:13:48Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• 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.