Fully-automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI by integrating a continuous max-flow model and a likelihood atlas

2013 ◽  
Author(s):  
Shandong Wu ◽  
Susan P. Weinstein ◽  
Emily F. Conant ◽  
Despina Kontos
Keyword(s):  
Density Estimation ◽  
Flow Model ◽  
Breast Mri ◽  
Tissue Segmentation ◽  
Volumetric Density ◽  
Fibroglandular Tissue

Clinical applicability and relevance of fibroglandular tissue segmentation on routine T1 weighted breast MRI

2017 ◽  
Vol 42 ◽  
pp. 119-125 ◽  
Author(s):  
Akshat C. Pujara ◽  
Artem Mikheev ◽  
Henry Rusinek ◽  
Harikrishna Rallapalli ◽  
Jerzy Walczyk ◽  
...  
Keyword(s):  
Breast Mri ◽  
Tissue Segmentation ◽  
Fibroglandular Tissue ◽  
Clinical Applicability

Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks

2020 ◽  
Vol 65 (10) ◽  
pp. 105006
Author(s):  
Xiangyuan Ma ◽  
Jinlong Wang ◽  
Xinpeng Zheng ◽  
Zhuangsheng Liu ◽  
Wansheng Long ◽  
...  
Keyword(s):  
Breast Mri ◽  
Generative Adversarial Networks ◽  
Tissue Segmentation ◽  
Fibroglandular Tissue ◽  
Adversarial Networks

scholarly journals Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net

2019 ◽  
Vol 26 (11) ◽  
pp. 1526-1535 ◽  
Author(s):  
Yang Zhang ◽  
Jeon-Hor Chen ◽  
Kai-Ting Chang ◽  
Vivian Youngjean Park ◽  
Min Jung Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Breast Mri ◽  
Tissue Segmentation ◽  
Fibroglandular Tissue

Fully-Automated Fibroglandular Tissue Segmentation in Breast MRI

2012 ◽  
pp. 244-251 ◽  
Author(s):  
Shandong Wu ◽  
Susan Weinstein ◽  
Brad M. Keller ◽  
Emily F. Conant ◽  
Despina Kontos
Keyword(s):  
Breast Mri ◽  
Tissue Segmentation ◽  
Fibroglandular Tissue

Automated artificial intelligence quantification of fibroglandular tissue on breast MRI.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e12071-e12071
Author(s):  
Ruquaiyah Takhtawala ◽  
Nataly Tapia Negrete ◽  
Madeleine Shaver ◽  
Turkay Kart ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Cross Validation ◽  
Pearson Correlation ◽  
Image Data ◽  
Breast Mri ◽  
Ground Truth ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Tissue Segmentation ◽  
Fibroglandular Tissue ◽  
Institutional Review

e12071 Background: The objective of this study is to examine if a convolutional neural network can be utilized to automate breast fibroglandular tissue segmentation, a risk factor for breast cancer, on MRIs. Methods: This institutional review board approved study assessed retrospectively acquired MRI T1 pre-contrast image data for 238 patients. Ground truth parameters were derived through manual segmentation. A hybrid 3D/2D U-Net architecture was developed for fibroglandular tissue segmentation. The network was trained with T1 pre-contrast MRI data and their corresponding ground-truth labels. The analysis was started with image pre-processing. Each MRI volume was re-sampled and normalized using z-scores. Convolution operations reduced 3D volumes into a 2D slice in the contracting arm of the U-Net architecture. Results: A 5-fold cross validation was performed and the Dice similarity coefficient was used to assess the accuracy of fibroglandular tissue segmentation. Cross-validation results showed that the automated hybrid CNN approach resulted in a Dice similarity coefficient of 0.848 and a Pearson correlation of 0.961 in comparison to the ground-truth for fibroglandular breast tissue segmentation, which demonstrates high accuracy. Conclusions: The results demonstrate significant application of deep learning in accurately automating segmentation of breast fibroglandular tissue.

Impact of menopausal status on background parenchymal enhancement and fibroglandular tissue on breast MRI

2012 ◽  
Vol 22 (12) ◽  
pp. 2641-2647 ◽  
Author(s):  
Valencia King ◽  
Yajia Gu ◽  
Jennifer B. Kaplan ◽  
Jennifer D. Brooks ◽  
Malcolm C. Pike ◽  
...  
Keyword(s):  
Menopausal Status ◽  
Breast Mri ◽  
Fibroglandular Tissue ◽  
Background Parenchymal Enhancement ◽  
Parenchymal Enhancement
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