A Domain Adaptation Model for Carotid Ultrasound: Image Harmonization, Noise Reduction, and Impact on Cardiovascular Risk Markers

• URL: http://arxiv.org/abs/2407.05163v1
• Date: Sat, 6 Jul 2024 19:44:00 GMT
• Title: A Domain Adaptation Model for Carotid Ultrasound: Image Harmonization, Noise Reduction, and Impact on Cardiovascular Risk Markers
• Authors: Mohd Usama, Emma Nyman, Ulf Naslund, Christer Gronlund,
• Abstract summary: We propose a Generative Adrial Network (GAN) based model for image-to-image translation in ultrasound images. We modified the texture pattern and reduced noise in Carotid ultrasound images while keeping the anatomy. The results showed that domain adaptation was achieved in both tasks.
• Score: 0.09999629695552192
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning has been used extensively for medical image analysis applications, assuming the training and test data adhere to the same probability distributions. However, a common challenge arises when dealing with medical images generated by different systems or even the same system with varying parameter settings. Such images often contain diverse textures and noise patterns, violating the assumption. Consequently, models trained on data from one machine or setting usually struggle to perform effectively on data from another. To address this issue in ultrasound images, we proposed a Generative Adversarial Network (GAN) based model in this paper. We formulated image harmonization and denoising tasks as an image-to-image translation task, wherein we modified the texture pattern and reduced noise in Carotid ultrasound images while keeping the image content (the anatomy) unchanged. The performance was evaluated using feature distribution and pixel-space similarity metrics. In addition, blood-to-tissue contrast and influence on computed risk markers (Gray scale median, GSM) were evaluated. The results showed that domain adaptation was achieved in both tasks (histogram correlation 0.920 and 0.844), as compared to no adaptation (0.890 and 0.707), and that the anatomy of the images was retained (structure similarity index measure of the arterial wall 0.71 and 0.80). In addition, the image noise level (contrast) did not change in the image harmonization task (-34.1 vs 35.2 dB) but was improved in the noise reduction task (-23.5 vs -46.7 dB). The model outperformed the CycleGAN in both tasks. Finally, the risk marker GSM increased by 7.6 (p<0.001) in task 1 but not in task 2. We conclude that domain translation models are powerful tools for ultrasound image improvement while retaining the underlying anatomy but that downstream calculations of risk markers may be affected.

Related papers

• Domain Adaptation of Carotid Ultrasound Images using Generative Adversarial Network [0.2624902795082451]
  We propose a novel Generative Adversarial Network (GAN)-based model for image-to-image translation.<n>We show that the model successfully translated the texture pattern of images and removed reverberation noise from the ultrasound images.
  arXiv  Detail & Related papers  (2026-01-04T10:08:36Z)
• Zero-Shot Low-dose CT Denoising via Sinogram Flicking [5.101848799297469]
  We propose a zero-shot low-dose CT imaging method based on sinogram flicking. By randomly swapping portions of the conjugate X-rays in the sinogram domain, we generate a large set of sinograms with consistent content but differing noise patterns. We train the network on pairs of sinograms with the same content but different noise distributions using a lightweight model adapted from ZS-NSN.
  arXiv  Detail & Related papers  (2025-04-10T17:42:01Z)
• Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models [3.3463490716514177]
  High frame-rate ultrasound imaging is a cutting-edge technique that enables high frame-rate imaging. One challenge associated with high frame-rate ultrasound imaging is the high noise associated with them, hindering their wider adoption. Our proposed solution aims to enhance plane wave image quality. Specifically, the method considers the distinction between low-angle and high-angle compounding plane waves as noise. In addition, our approach employs natural image segmentation masks as intensity maps for the generated images, resulting in accurate denoising for various anatomy shapes.
  arXiv  Detail & Related papers  (2024-08-20T16:31:31Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network [0.7868449549351486]
  Medical imaging plays a significant role in detecting and treating various diseases. We propose a retinal image enhancement method using a vision transformer and convolutional neural network.
  arXiv  Detail & Related papers  (2023-03-03T14:10:47Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• 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)
• Treatment Learning Causal Transformer for Noisy Image Classification [62.639851972495094]
  In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy. Motivated from causal variational inference, we propose a transformer-based architecture, that uses a latent generative model to estimate robust feature representations for noise image classification. We also create new noisy image datasets incorporating a wide range of noise factors for performance benchmarking.
  arXiv  Detail & Related papers  (2022-03-29T13:07:53Z)
• 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)
• Image translation of Ultrasound to Pseudo Anatomical Display Using Artificial Intelligence [0.0]
  CycleGAN was used to learn each domain properties separately and enforce cross domain cycle consistency. The generated pseudo anatomical images provide improved visual discrimination of the lesions with clearer border definition and pronounced contrast.
  arXiv  Detail & Related papers  (2022-02-16T13:31:49Z)
• Vision Transformers for femur fracture classification [59.99241204074268]
  The Vision Transformer (ViT) was able to correctly predict 83% of the test images. Good results were obtained in sub-fractures with the largest and richest dataset ever.
  arXiv  Detail & Related papers  (2021-08-07T10:12:42Z)
• Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation [8.920717493647121]
  We introduce a measure for machine perception inspired by the concept of Just Noticeable Difference (JND) of human perception. We suggest an adversarial image generation algorithm, which iteratively distorts an image by an additive noise until the machine learning model detects the change in the image by outputting a false label. We evaluate the adversarial images generated by our algorithm both qualitatively and quantitatively on CIFAR10, ImageNet, and MS COCO datasets.
  arXiv  Detail & Related papers  (2021-02-16T11:01:55Z)
• 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)

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.