scholarly journals Semi-automated segmentation and classification of digital breast tomosynthesis reconstructed images

2011 ◽  
Author(s):  
S. Vedantham ◽  
Linxi Shi ◽  
A. Karellas ◽  
K. E. Michaelsen ◽  
V. Krishnaswamy ◽  
...  
Keyword(s):  
Digital Breast Tomosynthesis ◽  
Automated Segmentation ◽  
Breast Tomosynthesis

TH-E-L100J-03: Digital Breast Tomosynthesis Mammography: Computerized Classification of Malignant and Benign Masses

2007 ◽  
Vol 34 (6Part24) ◽  
pp. 2645-2645 ◽  
Author(s):  
HP Chan ◽  
Y Wu ◽  
B Sahiner ◽  
Y Zhang ◽  
RH Moore ◽  
...  
Keyword(s):  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis

Detection and Classification of Calcifications on Digital Breast Tomosynthesis and 2D Digital Mammography: A Comparison

2011 ◽  
Vol 196 (2) ◽  
pp. 320-324 ◽  
Author(s):  
M. Lee Spangler ◽  
Margarita L. Zuley ◽  
Jules H. Sumkin ◽  
Gordan Abrams ◽  
Marie A. Ganott ◽  
...  
Keyword(s):  
Digital Mammography ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis

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

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Bingbing Xiao ◽  
Haotian Sun ◽  
You Meng ◽  
Yunsong Peng ◽  
Xiaodong Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Area Under The Curve ◽  
Digital Breast Tomosynthesis ◽  
False Positives ◽  
Imaging Method ◽  
Breast Tomosynthesis ◽  
Decision Level ◽  
Ensemble Strategy ◽  
Microcalcification Clusters

Abstract 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.

Comparison of synthetic and digital mammography with digital breast tomosynthesis or alone for the detection and classification of microcalcifications

2018 ◽  
Vol 29 (1) ◽  
pp. 319-329 ◽  
Author(s):  
Ji Soo Choi ◽  
Boo-Kyung Han ◽  
Eun Young Ko ◽  
Ga Ram Kim ◽  
Eun Sook Ko ◽  
...  
Keyword(s):  
Digital Mammography ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis

scholarly journals Significance and Application of Digital Breast Tomosynthesis for the BI-RADS Classification of Breast Cancer

2015 ◽  
Vol 16 (9) ◽  
pp. 4109-4114 ◽  
Author(s):  
Si-Qing Cai ◽  
Jian-Xiang Yan ◽  
Qing-Shi Chen ◽  
Mei-Ling Huang ◽  
Dong-Lu Cai
Keyword(s):  
Breast Cancer ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis

scholarly journals Comparison of radiation dose between 2D digital stereotactic versus digital breast tomosynthesis-guided breast biopsies

2021 ◽  
Vol 134 ◽  
pp. 109407
Author(s):  
T. Amir ◽  
S.P Zuckerman ◽  
B. Barufaldi ◽  
A.D Maidment ◽  
E.F Conant
Keyword(s):  
Radiation Dose ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis
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