BlindHarmony: "Blind" Harmonization for MR Images via Flow model

• URL: http://arxiv.org/abs/2305.10732v2
• Date: Wed, 16 Aug 2023 10:39:12 GMT
• Title: BlindHarmony: "Blind" Harmonization for MR Images via Flow model
• Authors: Hwihun Jeong, Heejoon Byun, Dong Un Kang, and Jongho Lee
• Abstract summary: In MRI, images of the same contrast from the same subject can exhibit noticeable differences when acquired using different hardware, sequences, or scan parameters. These differences create a domain gap that needs to be bridged by image harmonization. Deep learning-based approaches have been proposed to achieve image harmonization. We propose BlindHarmony, which utilizes only target domain data for training but still has the capability to harmonize images from unseen domains.
• Score: 1.765282368080009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In MRI, images of the same contrast (e.g., T$_1$) from the same subject can exhibit noticeable differences when acquired using different hardware, sequences, or scan parameters. These differences in images create a domain gap that needs to be bridged by a step called image harmonization, to process the images successfully using conventional or deep learning-based image analysis (e.g., segmentation). Several methods, including deep learning-based approaches, have been proposed to achieve image harmonization. However, they often require datasets from multiple domains for deep learning training and may still be unsuccessful when applied to images from unseen domains. To address this limitation, we propose a novel concept called `Blind Harmonization', which utilizes only target domain data for training but still has the capability to harmonize images from unseen domains. For the implementation of blind harmonization, we developed BlindHarmony using an unconditional flow model trained on target domain data. The harmonized image is optimized to have a correlation with the input source domain image while ensuring that the latent vector of the flow model is close to the center of the Gaussian distribution. BlindHarmony was evaluated on both simulated and real datasets and compared to conventional methods. BlindHarmony demonstrated noticeable performance on both datasets, highlighting its potential for future use in clinical settings. The source code is available at: https://github.com/SNU-LIST/BlindHarmony

Related papers

• DiffHarmony: Latent Diffusion Model Meets Image Harmonization [11.500358677234939]
  Diffusion models have promoted the rapid development of image-to-image translation tasks. Fine-tuning pre-trained latent diffusion models from scratch is computationally intensive. In this paper, we adapt a pre-trained latent diffusion model to the image harmonization task to generate harmonious but potentially blurry initial images.
  arXiv  Detail & Related papers  (2024-04-09T09:05:23Z)
• Improving Human-Object Interaction Detection via Virtual Image Learning [68.56682347374422]
  Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects. In this paper, we propose to alleviate the impact of such an unbalanced distribution via Virtual Image Leaning (VIL) A novel label-to-image approach, Multiple Steps Image Creation (MUSIC), is proposed to create a high-quality dataset that has a consistent distribution with real images.
  arXiv  Detail & Related papers  (2023-08-04T10:28:48Z)
• SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis [2.371982686172067]
  Different MRI scanners produce images with different characteristics, resulting in a domain shift known as the harmonisation problem' We propose an Unsupervised Source-Free Domain Adaptation (SFDA) method, SFHarmony, to overcome these barriers. Our method outperforms existing SFDA approaches across a range of realistic data scenarios.
  arXiv  Detail & Related papers  (2023-03-28T13:35:10Z)
• Painterly Image Harmonization in Dual Domains [13.067850524730698]
  We propose a novel painterly harmonization network consisting of a dual-domain generator and a dual-domain discriminator. The dual-domain generator performs harmonization by using AdaIN modules in the spatial domain and our proposed ResFFT modules in the frequency domain. The dual-domain discriminator attempts to distinguish the inharmonious patches based on the spatial feature and frequency feature of each patch, which can enhance the ability of generator in an adversarial manner.
  arXiv  Detail & Related papers  (2022-12-17T11:00:34Z)
• Decoupled Mixup for Generalized Visual Recognition [71.13734761715472]
  We propose a novel "Decoupled-Mixup" method to train CNN models for visual recognition. Our method decouples each image into discriminative and noise-prone regions, and then heterogeneously combines these regions to train CNN models. Experiment results show the high generalization performance of our method on testing data that are composed of unseen contexts.
  arXiv  Detail & Related papers  (2022-10-26T15:21:39Z)
• FRIH: Fine-grained Region-aware Image Harmonization [49.420765789360836]
  We propose a novel global-local two stages framework for Fine-grained Region-aware Image Harmonization (FRIH) Our algorithm achieves the best performance on iHarmony4 dataset (PSNR is 38.19 dB) with a lightweight model.
  arXiv  Detail & Related papers  (2022-05-13T04:50:26Z)
• Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation [49.59567529191423]
  We propose a novel semi-supervised learning framework for segmentation of volumetric images from new unlabeled domains. We jointly use supervised and contrastive learning, also introducing a contrastive pairing scheme that leverages similarity between nearby slices in 3D.
  arXiv  Detail & Related papers  (2022-03-07T19:02:26Z)
• SSH: A Self-Supervised Framework for Image Harmonization [97.16345684998788]
  We propose a novel Self-Supervised Harmonization framework (SSH) that can be trained using just "free" natural images without being edited. Our results show that the proposedSSH outperforms previous state-of-the-art methods in terms of reference metrics, visual quality, and subject user study.
  arXiv  Detail & Related papers  (2021-08-15T19:51:33Z)
• Diffusion-Based Representation Learning [65.55681678004038]
  We augment the denoising score matching framework to enable representation learning without any supervised signal. In contrast, the introduced diffusion-based representation learning relies on a new formulation of the denoising score matching objective. Using the same approach, we propose to learn an infinite-dimensional latent code that achieves improvements of state-of-the-art models on semi-supervised image classification.
  arXiv  Detail & Related papers  (2021-05-29T09:26:02Z)
• Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization [17.124438150480326]
  We study a methodology that aligns data from different domains in a nonlinear way through em kernelization. We successfully test KEMA in multi-temporal and multi-source very high resolution classification tasks, as well as on the task of making a model invariant to shadowing for hyperspectral imaging.
  arXiv  Detail & Related papers  (2020-12-07T08:46:11Z)
• Variational Clustering: Leveraging Variational Autoencoders for Image Clustering [8.465172258675763]
  Variational Autoencoders (VAEs) naturally lend themselves to learning data distributions in a latent space. We propose a method based on VAEs where we use a Gaussian Mixture prior to help cluster the images accurately. Our method simultaneously learns a prior that captures the latent distribution of the images and a posterior to help discriminate well between data points.
  arXiv  Detail & Related papers  (2020-05-10T09:34:48Z)

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.