Classification of microcalcifications using micro-CT

Temmermans, Frederik; Jansen, Bart; Willekens, Inneke; Casteele, Elke Van de; Deklerck, Rudi; Schelkens, Peter; Mey, Johan De

doi:10.1117/12.2026676

Cited by 4 publications

(6 citation statements)

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“…The tissue samples extracted have a diameter of 3 mm and a length of 23 mm. Further details are explained in [21,27].…”

Section: Breast Biopsymentioning

confidence: 99%

“…The X-ray detector (4000 x 2300) consisted of a gadolinium powder scintillator optically coupled with a tapered fiber to a cooled CCD sensor. Further information related to scanner settings can be found in [21,32]. For each sample, projection images were taken every 0.5°covering a view of 180°with an exposure time of 1.8 seconds per projection.…”

Section: Micro-ct Imagingmentioning

confidence: 99%

“…Since the most accurate and realistic way to determine characteristics of a 3D structure is to use a high resolution 3D imaging technique, attention has been paid to Xray micro-computed tomography (micro-CT). A relatively small number of studies has focused on high resolution 3D MCs characteristics to detect and diagnose breast cancer [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Initially, six 3D shape characteristics of 597 MCs (extracted from 11 samples) were analyzed and it was concluded that MCs belonging to malignant samples, have a more irregular shape compared to benign ones [20]. In a follow-up study on 100 samples, a promising automated sample classification system based only on eight shape and twelve boundary zone features [21] was proposed. A new classification approach (using the same dataset as in [21]) was later on proposed in [22] by clustering MCs based on their shape and texture features.…”

Section: Introductionmentioning

confidence: 99%

“…In a follow-up study on 100 samples, a promising automated sample classification system based only on eight shape and twelve boundary zone features [21] was proposed. A new classification approach (using the same dataset as in [21]) was later on proposed in [22] by clustering MCs based on their shape and texture features.…”

Section: Introductionmentioning

confidence: 99%

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Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images

et al. 2022

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Background The detection of suspicious microcalcifications on mammography represents one of the earliest signs of a malignant breast tumor. Assessing microcalcifications’ characteristics based on their appearance on 2D breast imaging modalities is in many cases challenging for radiologists. The aims of this study were to: (a) analyse the association of shape and texture properties of breast microcalcifications (extracted by scanning breast tissue with a high resolution 3D scanner) with malignancy, (b) evaluate microcalcifications’ potential to diagnose benign/malignant patients. Methods Biopsy samples of 94 female patients with suspicious microcalcifications detected during a mammography, were scanned using a micro-CT scanner at a resolution of 9 μm. Several preprocessing techniques were applied on 3504 extracted microcalcifications. A high amount of radiomic features were extracted in an attempt to capture differences among microcalcifications occurring in benign and malignant lesions. Machine learning algorithms were used to diagnose: (a) individual microcalcifications, (b) samples. For the samples, several methodologies to combine individual microcalcification results into sample results were evaluated. Results We could classify individual microcalcifications with 77.32% accuracy, 61.15% sensitivity and 89.76% specificity. At the sample level diagnosis, we achieved an accuracy of 84.04%, sensitivity of 86.27% and specificity of 81.39%. Conclusions By studying microcalcifications’ characteristics at a level of details beyond what is currently possible by using conventional breast imaging modalities, our classification results demonstrated a strong association between breast microcalcifications and malignancies. Microcalcification’s texture features extracted in transform domains, have higher discriminating power to classify benign/malignant individual microcalcifications and samples compared to pure shape-features.

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“…The tissue samples extracted have a diameter of 3 mm and a length of 23 mm. Further details are explained in [21,27].…”

Section: Breast Biopsymentioning

confidence: 99%

Section: Micro-ct Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images

et al. 2022

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Shape-Based Clustering and Classification of Breast Microcalcifications in Micro-CT Images

et al. 2015

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Microtomographic characterization of calcifications in canine mammary tumours

Reis

Oliveira

Pereira

et al. 2019

Vet Comparative Oncology

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The present work describes the microtomographic characterization of macro‐ and microcalcifications present in excised canine mammary glands. In human breast cancer, microcalcifications are highly relevant for diagnosis and prognosis, often being the sole element determining biopsy. Canine mammary tumours are considered a model for human breast cancer, but the morphological features of calcifications had still to be studied in this species. The objective of this research is to contribute to the characterization of the mineralization features of the canine mammary gland. In the present study, the excised mammary glands of 33 bitches underwent fluoroscopic examination. In 30 of the samples, the presence of calcification was suspected, and multiple biopsies were taken of these areas. Biopsy fragments underwent microtomographic scanning. Microcalcifications were found in non‐neoplastic glandular tissue, benign and malign lesions, as it is known to happen in humans. Qualitative evaluation regarding morphology of the imaged calcifications showed similarities to breast cancer findings, based on the BI‐RADS 2013 classification, such as pleomorphism and shape. No differences in the quantitative morphological parameters of volume, surface, surface/volume, SMI and structure thickness were found when macrocalcifications were considered. However, although significant differences existed in these parameters between microcalcifications from malignant canine mammary tumours and the two other groups, none were found between non‐neoplastic and benign tumours. Findings further support the use of this spontaneous animal model for the study of human breast cancer, considering how clinically relevant microcalcifications are in humans.

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Classification of microcalcifications using micro-CT

Cited by 4 publications

References 0 publications

Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images

Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images

Shape-Based Clustering and Classification of Breast Microcalcifications in Micro-CT Images

Microtomographic characterization of calcifications in canine mammary tumours

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