Scatter correction with deep learning approach for contrast enhanced digital breast tomosynthesis (CEDBT) in both cranio-caudal (CC) view and mediolateral oblique (MLO) view

2020 ◽  
Author(s):  
Xiaoyu Duan ◽  
Pranjal Sahu ◽  
Hailiang Huang ◽  
Wei Zhao
Keyword(s):  
Deep Learning ◽  
Scatter Correction ◽  
Digital Breast Tomosynthesis ◽  
Learning Approach ◽  
Breast Tomosynthesis ◽  
Contrast Enhanced

scholarly journals A scatter correction method for contrast-enhanced dual-energy digital breast tomosynthesis

2015 ◽  
Vol 60 (16) ◽  
pp. 6323-6354 ◽  
Author(s):  
Yihuan Lu ◽  
Boyu Peng ◽  
Beverly A Lau ◽  
Yue-Houng Hu ◽  
David A Scaduto ◽  
...  
Keyword(s):  
Scatter Correction ◽  
Digital Breast Tomosynthesis ◽  
Correction Method ◽  
Dual Energy ◽  
Breast Tomosynthesis ◽  
Contrast Enhanced

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

scholarly journals Optimization of a dual-energy contrast-enhanced technique for a photon-counting digital breast tomosynthesis system: I. A theoretical model

2010 ◽  
Vol 37 (11) ◽  
pp. 5896-5907 ◽  
Author(s):  
Ann-Katherine Carton ◽  
Christer Ullberg ◽  
Karin Lindman ◽  
Raymond Acciavatti ◽  
Tom Francke ◽  
...  
Keyword(s):  
Theoretical Model ◽  
Photon Counting ◽  
Digital Breast Tomosynthesis ◽  
Dual Energy ◽  
Breast Tomosynthesis ◽  
Contrast Enhanced

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.

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