Robust Scatterer Number Density Segmentation of Ultrasound Images

Tehrani, Ali K. Z.; Rosado-Méndez, Iván M.; Rivaz, Hassan

doi:10.1109/tuffc.2022.3144685

Cited by 11 publications

(10 citation statements)

References 44 publications

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“…A diverse dataset with known scatterer number density is required to train the network. We followed the fast grid-based method 6 with the difference that here the scatterer number density can be any value in the range of 1-20. Assuming weak scattering, the ultrasound RF data can be obtained by the 2D convolution of Point Spread Function (PSF) and the Tissue Reflectivity Function (TRF).…”

Section: Data Generationmentioning

confidence: 99%

“…Assuming weak scattering, the ultrasound RF data can be obtained by the 2D convolution of Point Spread Function (PSF) and the Tissue Reflectivity Function (TRF). 6,7 s (a,l) = T RF (a,l) * h (a,l)…”

Section: Data Generationmentioning

confidence: 99%

“…Different definition of the resolution cell results in different value of the scatterer number density. Please refer to 6 for more information about the grid-based simulation method. In order to reduce the correlation, samples are skipped from both axial and lateral directions to have an image of size 256 × 128, and an average correlation between samples of 0.28 is obtained.…”

Section: Data Generationmentioning

confidence: 99%

“…Unlike sampling from HK-distribution, this method of simulation data generation contains correlated samples which is a more realistic method and closer to the real ultrasound data. 6 In addition to this, multiple frames from experimental phantom data are employed to obtain a more accurate parametric images and quantify the uncertainty map of the network. The uncertainty map would enable the clinicians to find out how reliable the estimations are in each region.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A deep learning approach for patchless estimation of ultrasound quantitative parametric image with uncertainty measurement

Tehrani¹,

Rosado-Méndez²,

Whitson³

et al. 2023

Medical Imaging 2023: Ultrasonic Imaging and Tomography

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Quantitative ultrasound (QUS) aims to find properties of scatterers which are related to the tissue microstructure. Among different QUS parameters, scatterer number density has been found to be a reliable biomarker to detect different abnormalities. The homodyned K-distribution (HK-distribution) is a model for the probability density function of the ultrasound echo amplitude that can model different scattering scenarios but requires a large number of samples to be estimated reliably. Parametric images of HK-distribution parameters can be formed by dividing the envelope data into small overlapping patches and estimating parameters within the patches independently. This approach imposes two limiting constraints: the HK-distribution parameters are assumed to be constant within each patch, and each patch requires enough independent samples. In order to mitigate those problems, we employ a deep learning approach to estimate parametric images of scatterer number density (related to HK-distribution shape parameter) without patching. Furthermore, an uncertainty map of the network’s prediction is quantified to provide insight about the confidence of the network about the estimated HK parameter values.

show abstract

Section: Data Generationmentioning

confidence: 99%

Section: Data Generationmentioning

confidence: 99%

Section: Data Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A deep learning approach for patchless estimation of ultrasound quantitative parametric image with uncertainty measurement

Tehrani¹,

Rosado-Méndez²,

Whitson³

et al. 2023

Medical Imaging 2023: Ultrasonic Imaging and Tomography

View full text Add to dashboard Cite

show abstract

“…In this work, we have set the mask on non-artifact regions to construct paired clinical training datasets due to its feasibility, however, it is a novel but trade-off solution. Recently, Tehrani et al [52] proposed a novel promising method to handle the domain shift, they introduced a domain adaptation stage and the reference phantom is adopted to CNN for domain transformation to further enhance the performance, meanwhile, a simple but effective data generation scheme is introduced.…”

Section: Limitationsmentioning

confidence: 99%

AIVUS: Guidewire Artifacts Inpainting for Intravascular Ultrasound Imaging With United Spatiotemporal Aggregation Learning

Zhang

Cao

et al. 2022

IEEE Trans. Comput. Imaging

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The Intravascular Ultrasound (IVUS) technology is an important imaging modality used in realistic clinical practice, it is often combined with coronary angiography (CAG) to diagnose coronary artery disease. As the golden standard for in vivo imaging of coronary artery walls, it can provide high-resolution images of the artery wall. Generally, the IVUS acquisition device uses an ultrasonic transducer to acquire the fine-grained anatomical information of the cardiovascular tissue by means of pulse echo imaging. However, widely used mechanical rotating imaging system suffered from guidewire artifacts. The inadequate visualization caused by artifacts often caused huge trouble for clinical diagnosis and subsequent tissue structure evaluation, and there is no suitable way to solve this long-standing problem so far. In this paper, we conducted an exploratory study and proposed the first deep learning based network named AIVUS for repairing the corrupted IVUS images. The network has a novel generative adversarial architecture, the united of gated convolution and spatiotemporal aggregation structure has been introduced to enhance its restoration capability. The proposed network can handle large-scale, moving guidewire artifacts, and it can fully utilize spatial and temporal information hidden in sequence to recover the high-fidelity original content and maintain consistency between frames. Furthermore, we compared it with several latest restoration models, including both image restoration and video restoration models. Qualitative and quantitative comparison results on the collected IVUS datasets demonstrate that our method has achieved outstanding performance and its potential clinical value.

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Ultrasound normalized cumulative residual entropy imaging: Theory, methodology, and application

Gao,

Tsui,

et al. 2024

Computer Methods and Programs in Biomedicine

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Robust Scatterer Number Density Segmentation of Ultrasound Images

Cited by 11 publications

References 44 publications

A deep learning approach for patchless estimation of ultrasound quantitative parametric image with uncertainty measurement

A deep learning approach for patchless estimation of ultrasound quantitative parametric image with uncertainty measurement

AIVUS: Guidewire Artifacts Inpainting for Intravascular Ultrasound Imaging With United Spatiotemporal Aggregation Learning

Ultrasound normalized cumulative residual entropy imaging: Theory, methodology, and application

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