Differential Diagnosis of Microcalcifications

Lanyi, Marton

doi:10.1007/978-3-642-60919-0_7

Cited by 5 publications

(3 citation statements)

References 73 publications

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“…12 showing microcalcifications, which are early indicators of breast cancer in many women. 24 In the figure, the left image [ Fig. 12(a)] is created by magnifying a BPF reconstruction performed with pixels matching the size of the detector elements (140 μm).…”

Section: Resultsmentioning

confidence: 99%

Observation of super‐resolution in digital breast tomosynthesis

Acciavatti

Maidment

2012

Medical Physics

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Section: Resultsmentioning

confidence: 99%

Observation of super‐resolution in digital breast tomosynthesis

Acciavatti

Maidment

2012

Medical Physics

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“…Breast cancer has the highest morbidity and mortality among women's cancers [ 1 ], and early diagnosis and treatment can effectively improve the prognosis of breast cancer [ 2 ]. According to clinical statistics, 30% and 50% of breast cancers are accompanied by microcalcification clusters (MCs) [ 3 ]. As an important early manifestation of breast cancer, MCs is of great significance for early screening of breast cancer [ 4 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of microcalcification clusters in digital breast tomosynthesis using ensemble convolutional neural network

et al. 2021

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Background The classification of benign and malignant microcalcification clusters (MCs) is an important task for computer-aided diagnosis (CAD) of digital breast tomosynthesis (DBT) images. Influenced by imaging method, DBT has the characteristic of anisotropic resolution, in which the resolution of intra-slice and inter-slice is quite different. In addition, the sharpness of MCs in different slices of DBT is quite different, among which the clearest slice is called focus slice. These characteristics limit the performance of CAD algorithms based on standard 3D convolution neural network (CNN). Methods To make full use of the characteristics of the DBT, we proposed a new ensemble CNN, which consists of the 2D ResNet34 and the anisotropic 3D ResNet to extract the 2D focus slice features and 3D contextual features of MCs, respectively. Moreover, the anisotropic 3D convolution is used to build 3D ResNet to avoid the influence of DBT anisotropy. Results The proposed method was evaluated on 495 MCs in DBT images of 275 patients, which are collected from our collaborative hospital. The area under the curve (AUC) of receiver operating characteristic (ROC) and accuracy of classifying benign and malignant MCs using decision-level ensemble strategy were 0.8837 and 82.00%, which were significantly higher than the experimental results of 2D ResNet34 (AUC: 0.8264, ACC: 76.00%) and anisotropic 3D ResNet (AUC: 0.8455, ACC: 76.00%). Compared with the results of 3D features classification in the radiomics, the AUC of the deep learning method with decision-level ensemble strategy was improved by 0.0435, and the F1 score was improved from 79.37 to 85.71%. More importantly, the sensitivity increased from 78.13 to 84.38%, and the specificity increased from 66.67 to 77.78%, which effectively reduced the false positives of diagnosis Conclusion The results fully prove that the ensemble CNN can effectively integrate 2D features and 3D features, improve the classification performance of benign and malignant MCs in DBT, and reduce the false positives.

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“…Increasing the frequency of the object simulates small closely-spaced structures such as microcalcifications, which are early indicators of cancer in breast imaging applications. 8 The sine plate has led to the discovery of super-resolution in tomosynthesis. 1,5 Superresolution is a term which describes the ability to resolve input frequencies higher than the detector alias frequency, or the frequency above which high frequency information is represented as if it were low frequency information in a single projection.…”

Section: Introductionmentioning

confidence: 99%