Standardisation of Convex Ultrasound Data Through Geometric Analysis and Augmentation
- URL: http://arxiv.org/abs/2502.09482v1
- Date: Thu, 13 Feb 2025 16:45:39 GMT
- Title: Standardisation of Convex Ultrasound Data Through Geometric Analysis and Augmentation
- Authors: Alistair Weld, Giovanni Faoro, Luke Dixon, Sophie Camp, Arianna Menciassi, Stamatia Giannarou,
- Abstract summary: Ultrasound research and development has historically lagged, particularly in the case of applications with data-driven algorithms.
A significant issue with ultrasound is the extreme variability of the images, due to the number of different machines available.
The method proposed in this article is an approach to alleviating this issue of disorganisation.
- Score: 5.87276808100259
- License:
- Abstract: The application of ultrasound in healthcare has seen increased diversity and importance. Unlike other medical imaging modalities, ultrasound research and development has historically lagged, particularly in the case of applications with data-driven algorithms. A significant issue with ultrasound is the extreme variability of the images, due to the number of different machines available and the possible combination of parameter settings. One outcome of this is the lack of standardised and benchmarking ultrasound datasets. The method proposed in this article is an approach to alleviating this issue of disorganisation. For this purpose, the issue of ultrasound data sparsity is examined and a novel perspective, approach, and solution is proposed; involving the extraction of the underlying ultrasound plane within the image and representing it using annulus sector geometry. An application of this methodology is proposed, which is the extraction of scan lines and the linearisation of convex planes. Validation of the robustness of the proposed method is performed on both private and public data. The impact of deformation and the invertibility of augmentation using the estimated annulus sector parameters is also studied. Keywords: Ultrasound, Annulus Sector, Augmentation, Linearisation.
Related papers
- S-CycleGAN: Semantic Segmentation Enhanced CT-Ultrasound Image-to-Image Translation for Robotic Ultrasonography [2.07180164747172]
We introduce an advanced deep learning model, dubbed S-CycleGAN, which generates high-quality synthetic ultrasound images from computed tomography (CT) data.
The synthetic images are utilized to enhance various aspects of our development of the robot-assisted ultrasound scanning system.
arXiv Detail & Related papers (2024-06-03T10:53:45Z) - Ultrasound Imaging based on the Variance of a Diffusion Restoration Model [7.360352432782388]
We propose a hybrid reconstruction method combining an ultrasound linear direct model with a learning-based prior coming from a generative Denoising Diffusion model.
We conduct experiments on synthetic, in-vitro, and in-vivo data, demonstrating the efficacy of our variance imaging approach in achieving high-quality image reconstructions.
arXiv Detail & Related papers (2024-03-22T16:10:38Z) - CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images.
The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism.
We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
arXiv Detail & Related papers (2024-03-21T15:13:36Z) - UNICORN: Ultrasound Nakagami Imaging via Score Matching and Adaptation [59.91293113930909]
Nakagami imaging holds promise for visualizing and quantifying tissue scattering in ultrasound waves.
Existing methods struggle with optimal window size selection and suffer from estimator instability.
We propose a novel method called UNICORN that offers an accurate, closed-form estimator for Nakagami parameter estimation.
arXiv Detail & Related papers (2024-03-10T18:05:41Z) - 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) - AiAReSeg: Catheter Detection and Segmentation in Interventional
Ultrasound using Transformers [75.20925220246689]
endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature.
This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
arXiv Detail & Related papers (2023-09-25T19:34:12Z) - OADAT: Experimental and Synthetic Clinical Optoacoustic Data for
Standardized Image Processing [62.993663757843464]
Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion.
OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues.
No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
arXiv Detail & Related papers (2022-06-17T08:11:26Z) - Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions.
Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity.
A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
arXiv Detail & Related papers (2022-04-09T13:04:36Z) - Deep Learning for Ultrasound Beamforming [120.12255978513912]
Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, lies at the heart of the ultrasound image formation chain.
Modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing.
Deep learning methods can play a compelling role in the digital beamforming pipeline.
arXiv Detail & Related papers (2021-09-23T15:15:21Z) - Ultrasound Image Classification using ACGAN with Small Training Dataset [0.0]
Training deep learning models requires large labeled datasets, which is often unavailable for ultrasound images.
We exploit Generative Adversarial Network (ACGAN) that combines the benefits of large data augmentation and transfer learning.
We conduct experiment on a dataset of breast ultrasound images that shows the effectiveness of the proposed approach.
arXiv Detail & Related papers (2021-01-31T11:11:24Z) - Transducer Adaptive Ultrasound Volume Reconstruction [17.19369561039399]
3D volume reconstruction from freehand 2D scans is a very challenging problem, especially without the use of external tracking devices.
Recent deep learning based methods demonstrate the potential of directly estimating inter-frame motion between consecutive ultrasound frames.
We propose a novel domain adaptation strategy to adapt deep learning algorithms to data acquired with different transducers.
arXiv Detail & Related papers (2020-11-17T04:46:57Z)
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.