The creation of a large set of realistic synthetic microcalcification clusters for simulation in (contrast-enhanced) mammography images

Camp, Astrid Van; Cockmartin, Lesley; Beuque, Manon; Woodruff, Henry C.; Marshall, Nicholas; Lambin, Philippe; Bosmans, Hilde

doi:10.1117/12.2611393

Cited by 4 publications

(7 citation statements)

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“…Clusters were constrained to a size of 6 mm with 5-15 individual microcalcifications. Van Camp et al (2022) altered the edges of initial spheres by adding noise, followed by thresholding the resulting intensities in an attempt to create realistic, benign microcalcifications. Figure 1(b) shows such microcalcifications inserted in…”

Section: Microcalcification Clusters 211 Microcalcification Models Ba...mentioning

confidence: 99%

“…This microcalcification was resized and recombined five times to create a cluster which was then rotated. The same database formed the basis of the study by Van Camp et al (2022) where microcalcifications were recombined to create new malignant clusters. A large set of clusters could be created by applying random choices on the rotation and location of microcalcifications while still considering the properties of real clusters.…”

Section: From Images Of Biopsied Clustersmentioning

confidence: 99%

“…Carton et al (2003) and Zanca et al (2008) used these methods to modify initial templates for a range of system parameters. In other work, the hybrid tool developed by Shaheen et al (2010) for simulating 3D lesions was validated in depth by Vancoillie et al (2020) and applied in a number of studies (Shaheen et al 2011, Salvagnini et al 2016, Van Camp et al 2022. Similarly, Ho et al (2010) used the algorithm given by Tromans and Brandy (2010) to model image formation by considering the size and location of the microcalcifications.…”

Section: Hybrid Simulation Frameworkmentioning

confidence: 99%

“…A number of studies have examined the materials used to simulate microcalcifications, all expressing microcalcification attenuation properties as equivalent aluminium thickness (Carton et al 2004, Warren et al 2013. This relationship was later used by Van Camp et al (2022). Warren et al (2013) also compared the attenuation of CaOx and CaHa and found that microcalcifications could be modelled in VCTs using the attenuation coefficient of solid CaOx, weighted by a factor of 0.84.…”

Section: Simulation Parameters 351 Materials Composition Of Lesionsmentioning

confidence: 99%

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The creation of breast lesion models for mammographic virtual clinical trials: a topical review

et al. 2023

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Simulated breast lesion models, including microcalcification clusters and masses, have been used in several studies. Realistic lesions are required for virtual clinical trials (VCT) to be representative of clinical performance. Lesions can be obtained using mathematical methods or patient data. Various approaches exist to embed these lesions into breast structures to create cancer cases. This article provides a review of the literature available on the development of lesion models, simulation methods to insert these lesions into background structures and their applications.

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Section: Microcalcification Clusters 211 Microcalcification Models Ba...mentioning

confidence: 99%

Section: From Images Of Biopsied Clustersmentioning

confidence: 99%

Section: Hybrid Simulation Frameworkmentioning

confidence: 99%

Section: Simulation Parameters 351 Materials Composition Of Lesionsmentioning

confidence: 99%

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The creation of breast lesion models for mammographic virtual clinical trials: a topical review

et al. 2023

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“…Starting from a set of individual microcalcifications, new clusters were created based on mathematical properties and findings of the shape, size and distribution, as is described in [8]. Benign clusters had less microcalcifications, with their individual shape being more spherical whereas malignant clusters generally had more microcalcifications and the microcalcifications were more irregular in shape [9], [10].…”

Section: Generating Synthetic Datamentioning

confidence: 99%

Synthetic data of simulated microcalcification clusters to train and explain deep learning detection models in contrast-enhanced mammography

Camp¹,

Beuque

Cockmartin³

et al. 2022

16th International Workshop on Breast Imaging (IWBI2022)

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published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

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An automated toolbox for microcalcification cluster modeling for mammographic imaging

Van Camp,

Punter,

Houbrechts

et al. 2024

Medical Physics

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BackgroundMammographic imaging is essential for breast cancer detection and diagnosis. In addition to masses, calcifications are of concern and the early detection of breast cancer also heavily relies on the correct interpretation of suspicious microcalcification clusters. Even with advances in imaging and the introduction of novel techniques such as digital breast tomosynthesis and contrast‐enhanced mammography, a correct interpretation can still be challenging given the subtle nature and large variety of calcifications.PurposeComputer simulated lesion models can serve to develop, optimize, or improve imaging techniques. In addition to their use in comparative (virtual clinical trial) detection experiments, these models have potential application in training deep learning models and in the understanding and interpretation of breast lesions. Existing simulation methods, however, often lack the capacity to model the diversity occurring in breast lesions or to generate models relevant for a specific case. This study focuses on clusters of microcalcifications and introduces an automated, flexible toolbox designed to generate microcalcification cluster models customized to specific tasks.MethodsThe toolbox allows users to control a large number of simulation parameters related to model characteristics such as lesion size, calcification shape, or number of microcalcifications per cluster. This leads to the capability of creating models that range from regular to complex clusters. Based on the input parameters, which are either tuned manually or pre‐set for a specific clinical type, different sets of models can be simulated depending on the use case. Two lesion generation methods are described. The first method generates three‐dimensional microcalcification clusters models based on geometrical shapes and transformations. The second method creates two‐dimensional (2D) microcalcification cluster models for a specific 2D mammographic image. This novel method employs radiomics analysis to account for local textures, ensuring the simulated microcalcification cluster is appropriately integrated within the existing breast tissue. The toolbox is implemented in the Python language and can be conveniently run through a Jupyter Notebook interface, openly accessible at https://gitlab.kuleuven.be/medphysqa/deploy/breast‐calcifications. Validation studies performed by radiologists assessed the level of malignancy and realism of clusters tuned with specific parameters and inserted in mammographic images.ResultsThe flexibility of the toolbox with multiple simulation methods is illustrated, as well as the compatibility with different simulation frameworks and image types. The automation allows for the straightforward and fast generation of diverse microcalcification cluster models. The generated models are most likely applicable for various tasks as they can be configured in a variety of ways and inserted in different types of mammographic images of multiple acquisition systems. Validation studies confirmed the capacity to simulate realistic clusters and capture clinical properties when tuned with appropriate parameter settings.ConclusionThis simulation toolbox offers a flexible means of simulating microcalcification cluster models with potential use in both technical and clinical research in mammography imaging. The 3D generation methods allow for specifying many characteristics regarding the calcification shape and cluster architecture, and the 2D generation method presents a novel manner to create microcalcification clusters tailored to existing breast textures.

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The creation of a large set of realistic synthetic microcalcification clusters for simulation in (contrast-enhanced) mammography images

Abstract: The creation of a large set of realistic synthetic microcalcification clusters for simulation in (contrastenhanced) mammography images.

Cited by 4 publications

References 13 publications

The creation of breast lesion models for mammographic virtual clinical trials: a topical review

The creation of breast lesion models for mammographic virtual clinical trials: a topical review

Synthetic data of simulated microcalcification clusters to train and explain deep learning detection models in contrast-enhanced mammography

An automated toolbox for microcalcification cluster modeling for mammographic imaging

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