Image reconstruction from projections of digital breast tomosynthesis using deep learning

2021 ◽  
Author(s):  
Davi de Paula ◽  
Denis Salvadeo ◽  
Darlan de Araújo
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Image Reconstruction From Projections

scholarly journals A deep learning classifier for digital breast tomosynthesis

2021 ◽  
Vol 83 ◽  
pp. 184-193
Author(s):  
R. Ricciardi ◽  
G. Mettivier ◽  
M. Staffa ◽  
A. Sarno ◽  
G. Acampora ◽  
...  
Keyword(s):  
Deep Learning ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Learning Classifier

Backprojection Filtration Image Reconstruction Approach for Reducing High-Density Object Artifacts in Digital Breast Tomosynthesis

2019 ◽  
Vol 38 (5) ◽  
pp. 1161-1171 ◽  
Author(s):  
Hyeongseok Kim ◽  
Jongha Lee ◽  
Jeongtae Soh ◽  
Jonghwan Min ◽  
Young Wook Choi ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Digital Breast Tomosynthesis ◽  
High Density ◽  
Breast Tomosynthesis

scholarly journals Lesion Detection in Breast Tomosynthesis Using Efficient Deep Learning and Data Augmentation Techniques

2021 ◽  
Author(s):  
Loay Hassan ◽  
Mohamed Abedl-Nasser ◽  
Adel Saleh ◽  
Domenec Puig
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Breast Lesion ◽  
Data Augmentation ◽  
Digital Breast Tomosynthesis ◽  
Lesion Detection ◽  
Detection Methods ◽  
Breast Tomosynthesis ◽  
Mammographic Images ◽  
Augmentation Techniques

Digital breast tomosynthesis (DBT) is one of the powerful breast cancer screening technologies. DBT can improve the ability of radiologists to detect breast cancer, especially in the case of dense breasts, where it beats mammography. Although many automated methods were proposed to detect breast lesions in mammographic images, very few methods were proposed for DBT due to the unavailability of enough annotated DBT images for training object detectors. In this paper, we present fully automated deep-learning breast lesion detection methods. Specifically, we study the effectiveness of two data augmentation techniques (channel replication and channel-concatenation) with five state-of-the-art deep learning detection models. Our preliminary results on a challenging publically available DBT dataset showed that the channel-concatenation data augmentation technique can significantly improve the breast lesion detection results for deep learning-based breast lesion detectors.

Improving depth resolution in digital breast tomosynthesis by iterative image reconstruction

2015 ◽  
Author(s):  
Erin G. Roth ◽  
David N. Kraemer ◽  
Emil Y. Sidky ◽  
Ingrid S. Reiser ◽  
Xiaochuan Pan
Keyword(s):  
Image Reconstruction ◽  
Digital Breast Tomosynthesis ◽  
Depth Resolution ◽  
Iterative Image Reconstruction ◽  
Breast Tomosynthesis
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