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 3 publications

(7 citation statements)

References 13 publications

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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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Simulated image-specific microcalcification clusters and associated mass enhancement to enhance training of a deep learning model for cancer detection in contrast-enhanced mammography

Van Camp,

Woodruff,

Cockmartin

et al. 2024

17th International Workshop on Breast Imaging (IWBI 2024)

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We present an automated method to generate synthetic contrast-enhanced mammography cases with simulated microcalcification clusters. This method accounts for existing textures in the breast, with the simulated clusters inserted in the low-energy image. In parallel, potential mass-like enhancement is modelled from real values in the recombined image. The same deep learning model was trained with different amounts and ratios of real and synthetic data. When trained with real data only, malignant masses are more often correctly detected and classified than malignant microcalcification clusters. The addition of synthetic data with simulated clusters during training could increase detection sensitivity for all types of malignant lesions and maintained similar levels of AUC for classification. This enhanced performance was consistent on both internal and external test sets. These findings demonstrate the potential applicability of synthetic data to enhance deep learning models, especially when real data are scarce or imbalanced.

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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 3 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

Simulated image-specific microcalcification clusters and associated mass enhancement to enhance training of a deep learning model for cancer detection in contrast-enhanced mammography

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