scholarly journals Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method

2013 ◽  
Vol 40 (12) ◽  
pp. 122302 ◽  
Author(s):  
Shandong Wu ◽  
Susan P. Weinstein ◽  
Emily F. Conant ◽  
Despina Kontos
Keyword(s):  
Density Estimation ◽  
Breast Mri ◽  
Tissue Segmentation ◽  
Volumetric Density ◽  
Fuzzy C Means ◽  
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

Towards Accurate Segmentation of Fibroglandular Tissue in Breast MRI Using Fuzzy C-Means and Skin-Folds Removal

2015 ◽  
pp. 528-536 ◽  
Author(s):  
Mohammad Razavi ◽  
Lei Wang ◽  
Albert Gubern-Mérida ◽  
Tatyana Ivanovska ◽  
Hendrik Laue ◽  
...  
Keyword(s):  
Breast Mri ◽  
Fuzzy C Means ◽  
Fibroglandular Tissue

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.

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