Diffusion based multi-domain neuroimaging harmonization method with preservation of anatomical details
- URL: http://arxiv.org/abs/2409.00807v1
- Date: Sun, 1 Sep 2024 18:54:00 GMT
- Title: Diffusion based multi-domain neuroimaging harmonization method with preservation of anatomical details
- Authors: Haoyu Lan, Bino A. Varghese, Nasim Sheikh-Bahaei, Farshid Sepehrband, Arthur W Toga, Jeiran Choupan,
- Abstract summary: Multi-center neuroimaging studies face technical variability due to batch differences across sites.
Generative Adversarial Networks (GAN) has been a prominent method for addressing image harmonization tasks.
We have assessed the efficacy of the diffusion model for neuroimaging harmonization.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-center neuroimaging studies face technical variability due to batch differences across sites, which potentially hinders data aggregation and impacts study reliability.Recent efforts in neuroimaging harmonization have aimed to minimize these technical gaps and reduce technical variability across batches. While Generative Adversarial Networks (GAN) has been a prominent method for addressing image harmonization tasks, GAN-harmonized images suffer from artifacts or anatomical distortions. Given the advancements of denoising diffusion probabilistic model which produces high-fidelity images, we have assessed the efficacy of the diffusion model for neuroimaging harmonization. we have demonstrated the diffusion model's superior capability in harmonizing images from multiple domains, while GAN-based methods are limited to harmonizing images between two domains per model. Our experiments highlight that the learned domain invariant anatomical condition reinforces the model to accurately preserve the anatomical details while differentiating batch differences at each diffusion step. Our proposed method has been tested on two public neuroimaging dataset ADNI1 and ABIDE II, yielding harmonization results with consistent anatomy preservation and superior FID score compared to the GAN-based methods. We have conducted multiple analysis including extensive quantitative and qualitative evaluations against the baseline models, ablation study showcasing the benefits of the learned conditions, and improvements in the consistency of perivascular spaces (PVS) segmentation through harmonization.
Related papers
- Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound Imaging [32.99597899937902]
We propose a novel unsupervised anomaly detection framework based on a diffusion model.
The proposed framework incorporates a synthetic anomaly (Synomaly) noise function and a multi-stage diffusion process.
We validate the proposed approach on carotid US, brain MRI, and liver CT datasets.
arXiv Detail & Related papers (2024-11-06T15:43:51Z) - Disentangled Diffusion Autoencoder for Harmonization of Multi-site Neuroimaging Data [2.0431315722693344]
We introduce the disentangled diffusion autoencoder (DDAE), a novel diffusion model designed for controlling specific aspects of an image.
We demonstrate the DDAE's superiority in generating high-resolution, harmonized 2D MR images over previous approaches.
arXiv Detail & Related papers (2024-08-28T16:03:18Z) - StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model [62.25424831998405]
StealthDiffusion is a framework that modifies AI-generated images into high-quality, imperceptible adversarial examples.
It is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries.
arXiv Detail & Related papers (2024-08-11T01:22:29Z) - Diffusion Reconstruction of Ultrasound Images with Informative
Uncertainty [5.375425938215277]
Enhancing ultrasound image quality involves balancing concurrent factors like contrast, resolution, and speckle preservation.
We propose a hybrid approach leveraging advances in diffusion models.
We conduct comprehensive experiments on simulated, in-vitro, and in-vivo data, demonstrating the efficacy of our approach.
arXiv Detail & Related papers (2023-10-31T16:51:40Z) - DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic
Correlation Diffusion Model [46.68717345017946]
Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing.
We propose a novel unsupervised HSI-CD with semantic correlation diffusion model (DiffUCD)
Our method can achieve comparable results to those fully supervised methods requiring numerous samples.
arXiv Detail & Related papers (2023-05-21T09:21:41Z) - DiffMIC: Dual-Guidance Diffusion Network for Medical Image
Classification [32.67098520984195]
We propose the first diffusion-based model (named DiffMIC) to address general medical image classification.
Our experimental results demonstrate that DiffMIC outperforms state-of-the-art methods by a significant margin.
arXiv Detail & Related papers (2023-03-19T09:15:45Z) - The role of noise in denoising models for anomaly detection in medical
images [62.0532151156057]
Pathological brain lesions exhibit diverse appearance in brain images.
Unsupervised anomaly detection approaches have been proposed using only normal data for training.
We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
arXiv Detail & Related papers (2023-01-19T21:39:38Z) - SinDiffusion: Learning a Diffusion Model from a Single Natural Image [159.4285444680301]
We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image.
It is based on two core designs. First, SinDiffusion is trained with a single model at a single scale instead of multiple models with progressive growing of scales.
Second, we identify that a patch-level receptive field of the diffusion network is crucial and effective for capturing the image's patch statistics.
arXiv Detail & Related papers (2022-11-22T18:00:03Z) - Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images.
We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem.
Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
arXiv Detail & Related papers (2022-03-29T08:41:17Z) - Diffusion-Weighted Magnetic Resonance Brain Images Generation with
Generative Adversarial Networks and Variational Autoencoders: A Comparison
Study [55.78588835407174]
We show that high quality, diverse and realistic-looking diffusion-weighted magnetic resonance images can be synthesized using deep generative models.
We present two networks, the Introspective Variational Autoencoder and the Style-Based GAN, that qualify for data augmentation in the medical field.
arXiv Detail & Related papers (2020-06-24T18:00:01Z)
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.